falco-nlp.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oiiNxbvsm9Ia"
      },
      "source": [
        "# Generador de Reglas Falco\n",
        "Aplicación NLP que genera reglas Falco válidas mediante modelos de lenguaje, dada una descripción de una como entrada\n",
        "\n",
        "_Autor: Carel Sánchez_\n",
        "\n",
        "_Licencia: \t``GPL-3.0-only``_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qt6Uy1XRdS9L"
      },
      "source": [
        "### Fase 0: Requisitos previos\n",
        "Descarga los recursos necesarios y las rulesets de internet."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lusPH7hQlPy2"
      },
      "source": [
        "#### _Instalación de módulos_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SNKvGtmalPy2",
        "outputId": "703fb76d-fb3b-4a3e-96ff-2d7052647354"
      },
      "outputs": [],
      "source": [
        "!python -m pip install datasets pyyaml ipywidgets evaluate nltk accelerate rouge_score tiktoken\n",
        "!python -m pip install transformers[torch] -U #Resuelve un problema de HF\n",
        "!python -m pip install openai   #Para interactuar con GPT de OpenAI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cre4JshsnE-d"
      },
      "source": [
        "#### _Importamos las librerías necesarias_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1fIp31a0nFVU",
        "outputId": "8152bb6c-9309-489e-fd05-26bd59cbe482"
      },
      "outputs": [],
      "source": [
        "from torch import no_grad, cuda, from_numpy\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from tokenizers import Tokenizer\n",
        "from datasets import load_from_disk, Dataset\n",
        "from transformers import (\n",
        "    AutoModel,\n",
        "    AutoModelForSeq2SeqLM,\n",
        "    AutoConfig,\n",
        "    DataCollatorForSeq2Seq,\n",
        "    TrainingArguments,\n",
        "    Seq2SeqTrainingArguments,\n",
        "    Trainer,\n",
        "    Seq2SeqTrainer,\n",
        "    AutoTokenizer,\n",
        "    logging\n",
        ")\n",
        "from evaluate import load\n",
        "import nltk\n",
        "import numpy as np\n",
        "import re\n",
        "import yaml\n",
        "nltk.download('punkt')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4uMG6mEAGcD2"
      },
      "source": [
        "#### _Comprobamos especificaciones_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SzeG2DJlGkEh",
        "outputId": "39a4782f-c923-4f55-f2cf-9684f9fee81e"
      },
      "outputs": [],
      "source": [
        "import psutil\n",
        "from pathlib import Path\n",
        "\n",
        "def get_total_vram():\n",
        "    vram = !nvidia-smi.exe --query-gpu=memory.free --format=csv\n",
        "    return str(round(int(vram[-1].split()[0])/1024,2))+\" GB\"\n",
        "\n",
        "def get_total_ram():\n",
        "    return str(round(psutil.virtual_memory()[0]/(2**30),2))+\" GB\"\n",
        "\n",
        "def get_total_disk():\n",
        "    return str(round(psutil.disk_usage(\"/\")[0]/(2**30),2))+\" GB\"\n",
        "\n",
        "def get_used_vram():\n",
        "    vram = !nvidia-smi.exe --query-gpu=memory.used --format=csv\n",
        "    return str(round(int(vram[-1].split()[0])/1024,2))+\" GB\"\n",
        "\n",
        "def get_used_ram():\n",
        "    return str(round(psutil.virtual_memory()[3]/(2**30),2))+\" GB\"\n",
        "\n",
        "def get_used_disk():\n",
        "    return str(round(sum(f.stat().st_size for f in Path('.').glob('**/*') if f.is_file())/2**30,2))+\" GB\"\n",
        "\n",
        "def print_specs():\n",
        "    print(\"=== SPECS ===\")\n",
        "    print(\"Disk Size:\",get_total_disk())\n",
        "    print(\"RAM:      \",get_total_ram())\n",
        "    print(\"GPU RAM:  \",get_total_vram())\n",
        "\n",
        "def print_usage():\n",
        "    print(\"=== USAGE ===\")\n",
        "    print(\"Used Disk:\",get_used_disk())\n",
        "    print(\"Used RAM: \",get_used_ram())\n",
        "    print(\"Used VRAM:\",get_used_vram())\n",
        "\n",
        "\n",
        "print_specs()\n",
        "print_usage()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LxdOKDadlPy3"
      },
      "source": [
        "### Fase 1: Recopilación y creación de los Datasets\n",
        "\n",
        "Descargamos las rulesets desde distintas fuentes, unificamos, y creamos el dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bsAFFrfNFww4"
      },
      "source": [
        "#### Descarga de las rulesets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ulN_xshMm9Ic",
        "outputId": "26795f73-6109-4591-8dec-8224f016eacd"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "import os\n",
        "\n",
        "def download(uri:str, rule_source:list):\n",
        "    if not os.path.exists('files'):\n",
        "        os.mkdir(\"files\")\n",
        "\n",
        "    if not os.path.exists('files/rules'):\n",
        "        os.mkdir(\"files/rules\")\n",
        "\n",
        "    for rule in rule_source:\n",
        "        if not os.path.exists(\"files/rules/\"+rule):\n",
        "            print(f\"[*] Getting {rule}...\")\n",
        "            with open(\"files/rules/\"+rule,\"wb\") as f:\n",
        "                r = requests.get(uri_source+rule).text #Descarga el archivo\n",
        "                f.write(r.encode('utf8')) # Escribe y guarda el archivo\n",
        "        else:\n",
        "            print(f\"[!] The rule {rule} already exists!\")\n",
        "\n",
        "#Fuente 1: Repositorio oficial de Falco\n",
        "uri_source = \"https://raw.githubusercontent.com/falcosecurity/rules/main/rules/\"\n",
        "rule_source = [\"falco_rules.yaml\", \"falco-deprecated_rules.yaml\", \"falco-incubating_rules.yaml\", \"falco-sandbox_rules.yaml\"]\n",
        "download(uri_source,rule_source)\n",
        "\n",
        "#Fuente 2: Repositorio cloud-native-security\n",
        "uri_source = \"https://raw.githubusercontent.com/falcosecurity-retire/cloud-native-security-hub/master/resources/falco/\"\n",
        "rule_source = [\"admin.yaml\", \"apache.yaml\", \"consul.yaml\", \"elasticsearch.yaml\", \"etcd.yaml\", \"fim.yaml\", \"fluentd.yaml\", \"gke.yaml\", \"haproxy.yaml\", \"kubernetes.yaml\", \"mongo.yaml\", \"nginx.yaml\", \"php-fpm.yaml\", \"postgres.yaml\", \"redis.yaml\", \"rook.yaml\", \"ssh.yaml\", \"traefik.yaml\"]\n",
        "download(uri_source,rule_source)\n",
        "\n",
        "#Fuente 3: Repositorio falco_extended_rules\n",
        "uri_source = \"https://raw.githubusercontent.com/CloudDefenseAI/falco_extended_rules/master/rules/\"\n",
        "rule_source = [ \"account_manipulation_in_ssh.yaml\", \"archive_and_compression_activity.yaml\", \"attempt_to_access_bash_history.yaml\", \"boot_or_logon_autostart_execution.yaml\", \"chown_chmod_operations.yaml\", \"create_account_add_user.yaml\", \"create_nodeport_service.yaml\", \"create_or_modify_system_process.yaml\", \"credentials_from_password_file.yaml\", \"custom_rules.yaml\", \"detect_data_destruction_activity.yaml\", \"detect_peripheral_device_enumeration.yaml\", \"detect_service_disable.yaml\", \"detect_suspicious_disk_activity.yaml\", \"disable_recovery_features.yaml\", \"enumerate_domain_trust.yaml\", \"execute_command_via_utility.yaml\", \"get_info_about_open_application_windows.yaml\", \"memory_maps_of_processes.yaml\", \"modify_authentication_process.yaml\", \"password_policy_discovery.yaml\", \"permission_group_members_discovery.yaml\", \"process_injection.yaml\", \"read_disk_block_command.yaml\", \"suspicious_info_gathering.yaml\", \"suspicious_network_spanning_command.yaml\", \"suspicious_time_Date.yaml\", \"system_location_retrieval.yaml\", \"system_service_discovery.yaml\"]\n",
        "download(uri_source,rule_source)\n",
        "\n",
        "#Fuente 4: Repositorio Mitre-Attack-Falco-AWS\n",
        "uri_source = \"https://raw.githubusercontent.com/n1g3ld0ugla5/Mitre-Attack-Falco-AWS/main/plugins/rules/\"\n",
        "rule_source = [\"aws_cloudtrail_rules.yaml\"]\n",
        "download(uri_source,rule_source)\n",
        "\n",
        "#Fuente 5: Repositorio Sysdigfalcorules by hidd3ncod3s\n",
        "uri_source = \"https://raw.githubusercontent.com/hidd3ncod3s/sysdigfalcorules/master/rules/\"\n",
        "rule_source = [ \"BolTCMS.yaml\", \"CMS_MadeSimple.yaml\", \"Log1CMS.yaml\", \"MonstraCMS.yaml\", \"PolarBearCMS.yaml\", \"generic.yaml\", \"wordpress.yaml\"]\n",
        "download(uri_source,rule_source)\n",
        "\n",
        "#Fuente 6: Repositorio falco-rules-test\n",
        "uri_source = \"https://raw.githubusercontent.com/fsdaniel/falco-rules-test/main/\"\n",
        "rule_source = [\"global_falco_rules.yaml\"]\n",
        "download(uri_source,rule_source)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DJeq_86Jm9Ig"
      },
      "source": [
        "#### Creación del Dataset\n",
        "\n",
        " Esto lo haremos usando la librería ``datasets``, de HuggingFace\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XSPLIzV5m9Ig",
        "outputId": "46936b6b-473a-4861-b651-6a8ef060d7c2"
      },
      "outputs": [],
      "source": [
        "from datasets import (Dataset, DatasetDict, logging, disable_progress_bars)\n",
        "from os import listdir\n",
        "import yaml\n",
        "\n",
        "# Silenciamos el logging\n",
        "logging.set_verbosity_error()\n",
        "disable_progress_bars()\n",
        "\n",
        "#Función para extraer las reglas de las rulesets\n",
        "def retrieve_rules(path:str):\n",
        "    rules  = []\n",
        "    with open(path, 'r') as f:\n",
        "        ruleset = yaml.safe_load(f)\n",
        "        #Extraemos el verdadero ruleset si el objeto es un diccionario (esperamos una lista)\n",
        "        if type(ruleset) == dict:\n",
        "            ruleset = yaml.safe_load(ruleset['rules'][0]['raw'])\n",
        "\n",
        "        #Removing 'Nones'\n",
        "        while None in ruleset:\n",
        "            ruleset.remove(None)\n",
        "\n",
        "        #Extracting interesting items\n",
        "        for elem in ruleset[1:]:\n",
        "            if 'rule' in elem:\n",
        "                rules.append(elem)\n",
        "\n",
        "        return rules\n",
        "\n",
        "# Generador para dataset supervisado\n",
        "def gen():\n",
        "    for filepath in listdir(\"files/rules\"):\n",
        "        ruleset = retrieve_rules(\"files/rules/\"+filepath)\n",
        "        print(f\"The ruleset has {len(ruleset)} rules.\")\n",
        "        for rule in ruleset:\n",
        "            yield {\n",
        "                \"name\":rule['rule'],\n",
        "                \"description\":rule[\"desc\"],\n",
        "                \"condition\":rule[\"condition\"],\n",
        "                \"output\": rule[\"output\"],\n",
        "                \"priority\": rule[\"priority\"].upper(),\n",
        "            }\n",
        "\n",
        "# Generador para dataset no supervisado\n",
        "def gen_raw():\n",
        "    for filepath in listdir(\"files/rules\"):\n",
        "        ruleset = retrieve_rules(\"files/rules/\"+filepath)\n",
        "        print(f\"The ruleset '{filepath}' has {len(ruleset)} rules.\")\n",
        "        for rule in ruleset:\n",
        "            yield {\n",
        "                \"text\":yaml.dump(rule, sort_keys=False).strip()+\"\\n\"\n",
        "            }\n",
        "\n",
        "# Generador para dataset en crudo\n",
        "def gen_text():\n",
        "    for filepath in listdir(\"files/rules\"):\n",
        "        ruleset = retrieve_rules(\"files/rules/\"+filepath)\n",
        "        for rule in ruleset:\n",
        "            yield {\n",
        "                \"text\":(rule[\"desc\"] + \": \"+rule[\"condition\"]).replace(\"\\n\",\"\"),\n",
        "            }\n",
        "\n",
        "def gen_out():\n",
        "    for filepath in listdir(\"files/rules\"):\n",
        "        ruleset = retrieve_rules(\"files/rules/\"+filepath)\n",
        "        for rule in ruleset:\n",
        "            yield {\n",
        "                \"text\":(rule[\"desc\"] + \": \"+rule[\"output\"]).replace(\"\\n\",\"\"),\n",
        "            }\n",
        "\n",
        "def split(dataset):\n",
        "    trainTestValid = dataset.train_test_split(test_size=0.2)\n",
        "    TestValid = trainTestValid['test'].train_test_split(test_size=0.5)\n",
        "    return DatasetDict({\n",
        "        'train': trainTestValid['train'],\n",
        "        'test': TestValid['test'],\n",
        "        'validation':TestValid['train']\n",
        "    })\n",
        "\n",
        "# Version 1: Supervisado\n",
        "dataset = Dataset.from_generator(gen)\n",
        "dataset_split = split(dataset)\n",
        "\n",
        "# Version 2: No Supervisado\n",
        "dataset_text = Dataset.from_generator(gen_text)\n",
        "dataset_text_split = split(dataset_text)\n",
        "\n",
        "dataset_out = Dataset.from_generator(gen_out)\n",
        "dataset_out_split = split(dataset_out)\n",
        "\n",
        "# Version 3: Texto en Crudo\n",
        "dataset_raw = Dataset.from_generator(gen_raw)\n",
        "dataset_raw_split = split(dataset_raw)\n",
        "\n",
        "# Imprimimos los datasets\n",
        "print(\"dataset:\", dataset,\"----------\")\n",
        "print(\"dataset_text:\", dataset_text,\"----------\")\n",
        "print(\"dataset_out:\", dataset_out,\"----------\")\n",
        "print(\"dataset_raw:\", dataset_raw,\"----------\")\n",
        "print(\"dataset_split:\", dataset_split,\"----------\")\n",
        "print(\"dataset_text_split:\", dataset_text_split,\"----------\")\n",
        "print(\"dataset_out_split:\", dataset_out_split,\"----------\")\n",
        "print(\"dataset_raw_split:\", dataset_raw_split,\"----------\")\n",
        "\n",
        "# Guardamos los datasets\n",
        "dataset.save_to_disk(\"files/datasets/dataset\")\n",
        "dataset_text.save_to_disk(\"files/datasets/dataset_text\")\n",
        "dataset_out.save_to_disk(\"files/datasets/dataset_out\")\n",
        "dataset_raw.save_to_disk(\"files/datasets/dataset_raw\")\n",
        "\n",
        "dataset_split.save_to_disk(\"files/datasets/dataset_split\")\n",
        "dataset_text_split.save_to_disk(\"files/datasets/dataset_text_split\")\n",
        "dataset_out_split.save_to_disk(\"files/datasets/dataset_out_split\")\n",
        "dataset_raw_split.save_to_disk(\"files/datasets/dataset_raw_split\")\n",
        "\n",
        "print(\"All datasets have been saved\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gveX2usum9Ig"
      },
      "source": [
        "### Fase 2: Afinamiento del Modelo\n",
        "\n",
        "Afinamos la salida resultante del modelo a través de un entrenador usando un modelo como referencia."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jcyN0ckedS9R"
      },
      "source": [
        "#### Versión 1: Entrenamiento supervisado con Seq2Seq"
      ]
    },
    {
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      "outputs": [],
      "source": [
        "#@title\n",
        "from tokenizers import Tokenizer\n",
        "from datasets import load_from_disk, Dataset\n",
        "from transformers import (\n",
        "    AutoConfig,\n",
        "    AutoModelForSeq2SeqLM,\n",
        "    DataCollatorForSeq2Seq,\n",
        "    Seq2SeqTrainingArguments,\n",
        "    Seq2SeqTrainer,\n",
        "    AutoTokenizer\n",
        ")\n",
        "from evaluate import load\n",
        "import nltk\n",
        "import numpy as np\n",
        "import yaml\n",
        "nltk.download('punkt')\n",
        "\n",
        "# Función de afinamiento de modelo\n",
        "def train_v1(dataset: Dataset, model_checkpoint: str, tokenizer_checkpoint: str, input_key: str, output_key: str, version = \"v1\"):\n",
        "    print(\"Loading the model and Tokenizer...\")\n",
        "    #Cargamos el modelo y el tokenizador\n",
        "    config = AutoConfig.from_pretrained(model_checkpoint)\n",
        "    #Usaremos Seq2SeqTrainer (optimizado para la sumarización)\n",
        "    model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)\n",
        "\n",
        "    tokenizer = AutoTokenizer.from_pretrained(tokenizer_checkpoint)\n",
        "    #Añadimos el Padding Token\n",
        "    if tokenizer.pad_token is None:\n",
        "        tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n",
        "\n",
        "    #Tokenizamos el dataset\n",
        "    print(\"Tokenizing the dataset...\")\n",
        "    def tokenize_function(examples):\n",
        "        if model_checkpoint in [\"t5-small\", \"t5-base\", \"t5-larg\", \"t5-3b\", \"t5-11b\"]:\n",
        "            prefix = \"summarize: \"\n",
        "        else:\n",
        "            prefix = \"\"\n",
        "        max_input_length = 1024\n",
        "        max_target_length = 128\n",
        "        inputs = [prefix + doc for doc in examples[input_key]]\n",
        "        model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)\n",
        "\n",
        "        # Inicializamos el tokenizador para los targets\n",
        "        labels = tokenizer(text_target=examples[output_key],\n",
        "                           max_length=max_target_length,\n",
        "                           truncation=True)\n",
        "\n",
        "        model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "        return model_inputs\n",
        "\n",
        "    tokenized_dataset = dataset.map(tokenize_function, batched=True)\n",
        "\n",
        "    #Creamos la métrica\n",
        "    def compute_metrics(eval_pred):\n",
        "        #Sacado del Notebook de ejemplo de HF 'Summarization'\n",
        "        predictions, labels = eval_pred\n",
        "        decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
        "\n",
        "        #Usaremos la métrica ROUGE\n",
        "        metric = load(\"rouge\")\n",
        "\n",
        "        # Reemplazamos -100 de los labels porque no se pueden decodificar.\n",
        "        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "        decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "\n",
        "        # Por cada frase, Rouge se espera un salto de línea\n",
        "        decoded_preds = [\"\\n\".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_preds]\n",
        "        decoded_labels = [\"\\n\".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels]\n",
        "\n",
        "        # La métrica ROUGE usa el parámetro `use_aggregator` que devuelve una\n",
        "        # lista de la métrica calculada de cada frase, pero no suele ser usado\n",
        "        # en otras métricas\n",
        "        result = metric.compute(\n",
        "            predictions=decoded_preds,\n",
        "            references=decoded_labels,\n",
        "            use_stemmer=True,\n",
        "            use_aggregator=True\n",
        "            )\n",
        "\n",
        "        # Extraemos algunos resultados\n",
        "        result = {key: value * 100 for key, value in result.items()}\n",
        "\n",
        "        # Añadimos la longitud media de la salida generada\n",
        "        prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions]\n",
        "        result[\"gen_len\"] = np.mean(prediction_lens)\n",
        "\n",
        "        #Devolvemos los resultados redondeados en cuatro decimales\n",
        "        return {k: round(v, 4) for k, v in result.items()}\n",
        "\n",
        "    batch_size = 16                            #Definimos el tamaño de los lotes\n",
        "    model_name = model_checkpoint.split(\"/\")[-1] #Extraemos el nombre del modelo\n",
        "\n",
        "    # Establecemos los argumentos\n",
        "    args = Seq2SeqTrainingArguments(\n",
        "        f\"{model_name}-rulegen\"+\"-\"+version,   #Nombre del modelo resultante\n",
        "        eval_strategy = \"epoch\",\n",
        "        learning_rate=2e-5,\n",
        "        per_device_train_batch_size=batch_size,\n",
        "        per_device_eval_batch_size=batch_size,\n",
        "        weight_decay=0.01,\n",
        "        save_total_limit=3,\n",
        "        num_train_epochs=1,\n",
        "        predict_with_generate=True,\n",
        "        fp16=True,\n",
        "        push_to_hub=False,\n",
        "    )\n",
        "\n",
        "    # Creamos el colador de datos\n",
        "    data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)\n",
        "\n",
        "    trainer = Seq2SeqTrainer(\n",
        "        model,\n",
        "        args,\n",
        "        train_dataset=tokenized_dataset[\"train\"],\n",
        "        eval_dataset=tokenized_dataset[\"validation\"],\n",
        "        data_collator=data_collator,\n",
        "        tokenizer=tokenizer,\n",
        "        compute_metrics=compute_metrics,\n",
        "    )\n",
        "\n",
        "    #Entrenamos el modelo\n",
        "    print(\"Running the training process...\")\n",
        "    trainer.train()\n",
        "\n",
        "    #Devolvemos el entrenador\n",
        "    return trainer\n",
        "\n",
        "# Realizamos el afinamiento del modelo mediante entrenamiento\n",
        "dataset = load_from_disk(\"files/datasets/dataset_split\")\n",
        "\n",
        "model_checkpoint_v1 = \"google-t5/t5-small\" #@param  {type:\"string\"}\n",
        "tokenizer_checkpoint_v1 = \"google-t5/t5-small\" #@param  {type:\"string\"}\n",
        "\n",
        "print(\"Initiating training script...\")\n",
        "rule_tf = train_v1(dataset, model_checkpoint_v1, tokenizer_checkpoint_v1, \"description\", \"condition\", \"v1-condition\")\n",
        "rule_tf.save_model()\n",
        "print(\"Model has been fine-tuned and saved!\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KjXONHAGGxOk"
      },
      "source": [
        "_Obtenemos el consumo y liberamos memoria_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pD7M_NFbHAEl",
        "outputId": "3f2668cf-83a8-4adf-9002-9e7818de83db"
      },
      "outputs": [],
      "source": [
        "print_usage()\n",
        "del rule_tf\n",
        "cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "53rgwHe2dS9S"
      },
      "source": [
        "#### Versión 2: Entrenamiento sin supervisión"
      ]
    },
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        },
        "id": "LKNSsRv3dS9S",
        "outputId": "50030d28-ec9c-4ad2-e35e-95459b9e0056"
      },
      "outputs": [],
      "source": [
        "#@title\n",
        "from transformers import (\n",
        "    AutoTokenizer,\n",
        "    AutoModelForCausalLM,\n",
        "    Trainer,\n",
        "    TrainingArguments,\n",
        "    DataCollatorForLanguageModeling\n",
        "    )\n",
        "\n",
        "from datasets import load_from_disk\n",
        "import evaluate\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "def train_v2(dataset, model_checkpoint: str, tokenizer_checkpoint: str, version = \"v2\"):\n",
        "\n",
        "    print(\"Loading model and tokenizer...\")\n",
        "    # Cargar el tokenizador y el modelo preentrenado\n",
        "    tokenizer = AutoTokenizer.from_pretrained(tokenizer_checkpoint)\n",
        "    if tokenizer.pad_token is None:\n",
        "        tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n",
        "\n",
        "    model = AutoModelForCausalLM.from_pretrained(model_checkpoint)\n",
        "    model.to('cuda') # Forces to use CUDA\n",
        "\n",
        "    # Tokeniza los datos\n",
        "    def tokenize_function(examples):\n",
        "        max_input_length = 1024\n",
        "        return tokenizer(examples['text'],\n",
        "                         max_length=max_input_length,\n",
        "                         padding='max_length',\n",
        "                         truncation=True\n",
        "                         )\n",
        "\n",
        "    # Crea los grupos de texto\n",
        "    def group_texts(examples):\n",
        "        block_size = 128        #Tamaño de los grupos de texto\n",
        "        # Concatenamos el texto\n",
        "        concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\n",
        "        total_length = len(concatenated_examples[list(examples.keys())[0]])\n",
        "        #Calculamos longitud total\n",
        "        total_length = (total_length // block_size) * block_size\n",
        "        # Separamos el texto en trozos del tamaño de block_size\n",
        "        result = {\n",
        "            k: [t[i : i + block_size] for i in range(0, total_length, block_size)]\n",
        "            for k, t in concatenated_examples.items()\n",
        "        }\n",
        "        result[\"labels\"] = result[\"input_ids\"].copy()\n",
        "        return result\n",
        "\n",
        "    # Tokenizamos y hacemos bloques de texto\n",
        "    print(\"Tokenizing and group-texting the dataset...\")\n",
        "    tokenized_dataset = dataset.map(\n",
        "        tokenize_function,\n",
        "        batched=True,\n",
        "        num_proc = 4,\n",
        "        remove_columns=['text']\n",
        "    )\n",
        "    lm_dataset = tokenized_dataset.map(group_texts, batched=True, num_proc=4)\n",
        "\n",
        "    model_name = model_checkpoint.split(\"/\")[-1]\n",
        "    # Configuración del entrenamiento\n",
        "    training_args = TrainingArguments(\n",
        "        output_dir=f\"{model_name}-rulegen\"+\"-\"+version,\n",
        "        overwrite_output_dir=True,\n",
        "        eval_strategy=\"epoch\",\n",
        "        learning_rate=2e-5,\n",
        "        weight_decay=0.01,\n",
        "        fp16=True,\n",
        "        use_cpu = False,\n",
        "    )\n",
        "    # Inicializar el entrenador\n",
        "    trainer = Trainer(\n",
        "        model=model,\n",
        "        args=training_args,\n",
        "        train_dataset=lm_dataset['train'],\n",
        "        eval_dataset=lm_dataset['validation'],\n",
        "    )\n",
        "\n",
        "    # Entrenar el modelo\n",
        "    print(\"Starting training process...\")\n",
        "    trainer.train()\n",
        "    return trainer\n",
        "\n",
        "\n",
        "### Realizamos el afinamiento del modelo mediate entrenamiento ###\n",
        "\n",
        "#Dataset para generar la condición\n",
        "dataset_text_split = load_from_disk(\"files/datasets/dataset_text_split\")\n",
        "#Dataset para generar la salida\n",
        "dataset_out_split = load_from_disk(\"files/datasets/dataset_out_split\")\n",
        "#Modelo a afinar\n",
        "model_checkpoint = \"gpt2\" #@param  {type:\"string\"}\n",
        "#Tokenizador a usar\n",
        "tokenizer_checkpoint = \"sgugger/gpt2-like-tokenizer\" #@param  {type:\"string\"}\n",
        "\n",
        "# Entrenamos el modelo...\n",
        "print(\"Initiating training script...\")\n",
        "rule_tf = train_v2(dataset_text_split, model_checkpoint, tokenizer_checkpoint, \"v2-condition\")\n",
        "rule_tf.save_model()\n",
        "print(\"Model has been fine-tuned and saved!\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lx-koFMalPy9"
      },
      "source": [
        "_Evaluamos la perplejidad, el consumo, y liberamos memoria:_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gVb9UkfvlPy9",
        "outputId": "34c3ac7e-c902-4284-aa14-2cd689f02eed"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "eval_results = rule_tf.evaluate()\n",
        "print_usage()\n",
        "print(f\"Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")\n",
        "del rule_tf\n",
        "cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dBZyrj3odS9S"
      },
      "source": [
        "#### Versión 3: Solamente texto en crudo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1jhG87D3dS9S",
        "outputId": "ddb30d2c-bc1f-4aeb-a0ae-dfba958e8598"
      },
      "outputs": [],
      "source": [
        "# Es la misma versión que la versión 2,\n",
        "# solo que usamos las reglas enteras en crudo\n",
        "\n",
        "#@title\n",
        "dataset_raw_split = load_from_disk(\"files/datasets/dataset_raw_split\")\n",
        "model_checkpoint = \"gpt2\" #@param  {type:\"string\"}\n",
        "tokenizer_checkpoint = \"sgugger/gpt2-like-tokenizer\" #@param  {type:\"string\"}\n",
        "print(\"Initiating training script...\")\n",
        "rule_tf = train_v2(dataset_raw_split, model_checkpoint, tokenizer_checkpoint, \"v3\")\n",
        "rule_tf.save_model()\n",
        "print(\"Model has been fine-tuned and saved!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xwdUdXualPy-"
      },
      "source": [
        "_Evaluamos  la perplejidad, el consumo, y liberamos memoria:_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ARmBqij7lPy-",
        "outputId": "633fd469-a6a7-487b-870e-c197e135b021"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "eval_results = rule_tf.evaluate()\n",
        "print_usage()\n",
        "print(f\"Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")\n",
        "del rule_tf\n",
        "cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LlEslRexFww_"
      },
      "source": [
        "#### Generador de reglas por LLM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CSyKM1sdFww_"
      },
      "outputs": [],
      "source": [
        "from openai import OpenAI\n",
        "\n",
        "# Es necesario haber definido como variable de entorno la clave de acceso a la API de OpenAI\n",
        "def gpt_generate_rule(description: str):\n",
        "    api_key=\"insert_your_api_key_here\" #Usar aquí tu llave API de OpenAI\n",
        "    client = OpenAI(api_key=api_key)\n",
        "\n",
        "    # Indicamos el modelo que queremos usar\n",
        "    model = \"gpt-4o\"\n",
        "    input_price = 0.005\n",
        "    output_price = 0.015\n",
        "    # Pre-definimos el prompt\n",
        "    messages=[\n",
        "        # Instrucciones que damos al LLM\n",
        "        {\n",
        "        \"role\": \"system\",\n",
        "        \"content\": [\n",
        "            {\n",
        "            \"type\": \"text\",\n",
        "            \"text\": \"You are a security engineer, and your task is to automate the generation of Falco rules to detect and prevent various attack techniques. You will receive a description of an attack technique, and you need to produce a corresponding Falco rule in YAML format. Guidelines for generating the Falco rule: \\t\\n- Rule Definition: \\t\\t\\n        - Define a rule name that briefly describes the detection objective.\\n \\t- Provide a description (desc) of what the rule is detecting and why it's important.\\n        - Mandatory fields are \\\"Rule\\\", \\\"Description\\\", \\\"Condition\\\", \\\"Output\\\" and \\\"Priority\\\", in that order. \\\"Rule\\\" field is the header of the Rule object, and contains the name of the rule, while the rest of fields are sub-fields of the Rule object\\n - Condition: \\n\\t- Use appropriate conditions to detect the described attack technique. \\n\\t- Include relevant process names, arguments, and context (such as user IDs or working directories). \\n\\t- Ensure the conditions are specific enough to avoid false positives but general enough to catch the described attack. \\n- Output: \\n\\t- Craft an output message that clearly states what was detected, including relevant details such as the command line and user name.  Use up to 50 words\\n- Priority and Tags: \\n\\t\\t- Set an appropriate priority level (info, warning, critical). Include relevant tags for categorization (e.g., [security, intrusion]).\\n\\nIt is expected the user will submit only a description for a specific rule.  Give only the Falco rule in YAML format. Do not comment it nor use Markdown. Once you finished, in a new line write [END]\"\n",
        "            }\n",
        "        ]\n",
        "        },\n",
        "        # Ofrecemos una entrada de ejemplo\n",
        "        {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": [\n",
        "            {\n",
        "            \"type\": \"text\",\n",
        "            \"text\": \"Please generate a Falco Rule for the following description:\\nA shell was used as the entrypoint/exec point into a container with an attached terminal. Parent process may have  legitimately already exited and be null (read container_entrypoint macro). Common when using \\\"kubectl exec\\\" in Kubernetes.  Correlate with k8saudit exec logs if possible to find user or serviceaccount token used (fuzzy correlation by namespace and pod name).  Rather than considering it a standalone rule, it may be best used as generic auditing rule while examining other triggered  rules in this container/tty.\"\n",
        "            }\n",
        "        ]\n",
        "        },\n",
        "        # Ofrecemos una salida de ejemplo\n",
        "        {\n",
        "        \"role\": \"assistant\",\n",
        "        \"content\": [\n",
        "            {\n",
        "            \"type\": \"text\",\n",
        "            \"text\": \"- rule: Terminal shell in container\\r\\n  desc: >\\r\\n    A shell was used as the entrypoint/exec point into a container with an attached terminal. Parent process may have \\r\\n    legitimately already exited and be null (read container_entrypoint macro). Common when using \\\"kubectl exec\\\" in Kubernetes. \\r\\n    Correlate with k8saudit exec logs if possible to find user or serviceaccount token used (fuzzy correlation by namespace and pod name). \\r\\n    Rather than considering it a standalone rule, it may be best used as generic auditing rule while examining other triggered \\r\\n    rules in this container/tty.\\r\\n  condition: >\\r\\n    spawned_process \\r\\n    and container\\r\\n    and shell_procs \\r\\n    and proc.tty != 0\\r\\n    and container_entrypoint\\r\\n    and not (never_true)\\r\\n  output: A shell was spawned in a container with an attached terminal (evt_type=%evt.type user=%user.name user_uid=%user.uid user_loginuid=%user.loginuid process=%proc.name proc_exepath=%proc.exepath parent=%proc.pname command=%proc.cmdline terminal=%proc.tty exe_flags=%evt.arg.flags %container.info)\\r\\n  priority: NOTICE\\r\\n  tags: [maturity_stable, container, shell, mitre_execution, T1059]\"\n",
        "            }\n",
        "        ]\n",
        "        },\n",
        "        # Aquí incluimos el prompt con la descripción de la regla que queremos generar\n",
        "        {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": [\n",
        "            {\n",
        "            \"type\": \"text\",\n",
        "            \"text\": \"Please generate a Falco Rule for the following description:\\n\"+description\n",
        "            }\n",
        "        ]\n",
        "        },\n",
        "        ]\n",
        "\n",
        "    # Parámetros de generación\n",
        "    temperature=1\n",
        "    temp = 1\n",
        "    max_t=1500\n",
        "    top_p=1\n",
        "    freq_p=0\n",
        "    pres_p=0\n",
        "    stop=[\"[END]\"]\n",
        "\n",
        "    # Enviamos la petición a OpenAI y devolvemos la regla\n",
        "    response = client.chat.completions.create(model=model, messages=messages, temperature=temp, max_tokens=max_t, top_p=top_p, frequency_penalty=freq_p, presence_penalty=pres_p,stop=stop)\n",
        "    prompt_tokens = response.usage.prompt_tokens\n",
        "    completion_tokens = response.usage.completion_tokens\n",
        "    price = prompt_tokens*(input_price/1000)+completion_tokens*(output_price/1000)\n",
        "    return (response.choices[0].message.content, price)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OaFwelqOpMRO"
      },
      "source": [
        "#### Creación de otros modelos\n",
        "Las versiones 1 y 2 requieren crear otros modelos para completar una regla Falco\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "PcCjirLgpeVG",
        "outputId": "9db610be-3f57-4426-f5de-7fd50b795363"
      },
      "outputs": [],
      "source": [
        "#@title\n",
        "import nltk\n",
        "import numpy as np\n",
        "import yaml\n",
        "from datasets import load_from_disk, concatenate_datasets\n",
        "from evaluate import load\n",
        "from torch import from_numpy, nn\n",
        "from transformers import (DataCollatorWithPadding,\n",
        "                          AutoTokenizer,\n",
        "                          Trainer,\n",
        "                          TrainingArguments,\n",
        "                          AutoModelForSequenceClassification)\n",
        "\n",
        "nltk.download('punkt')\n",
        "\n",
        "# Modelo para generar la salida.\n",
        "# Cada uno usa una versión del entrenador\n",
        "dataset_split = load_from_disk(\"files/datasets/dataset_split\")\n",
        "dataset_out_split = load_from_disk(\"files/datasets/dataset_out_split\")\n",
        "print(\"[*] Creating V1 output generator model...\")\n",
        "rule_out_v1 =train_v1(dataset_split,\n",
        "                      model_checkpoint_v1,\n",
        "                      tokenizer_checkpoint_v1,\n",
        "                      \"description\",\n",
        "                      \"output\",\n",
        "                      \"v1-output\")\n",
        "print(\"[*]  Creating V2 output generator model...\")\n",
        "rule_out_v2 = train_v2(dataset_out_split,\n",
        "                       model_checkpoint,\n",
        "                       tokenizer_checkpoint,\n",
        "                       \"v2-output\")\n",
        "\n",
        "rule_out_v1.save_model()\n",
        "rule_out_v2.save_model()\n",
        "\n",
        "del rule_out_v1, rule_out_v2\n",
        "cuda.empty_cache()\n",
        "\n",
        "# Modelo para clasificar por prioridad\n",
        "# Creamos un entrenador propio\n",
        "\n",
        "pr_model_checkpoint = \"distilbert-base-uncased\" #@param {type:\"string\"}\n",
        "pr_tokenizer_checkpoint = \"distilbert-base-uncased\" #@param {type:\"string\"}\n",
        "\n",
        "def train_priority(dataset):\n",
        "    # Definimos las etiquetas, de ida y de vuelta\n",
        "    priority_label = {\"EMERGENCY\":0, \"ALERT\":1, \"CRITICAL\":2,\n",
        "                   \"ERROR\":3, \"WARNING\":4, \"NOTICE\":5,\n",
        "                   \"INFO\":6, \"DEBUG\":7}\n",
        "    label_priority = {}\n",
        "    for l, i in zip(list(priority_label.keys()),[*range(len(priority_label))]):\n",
        "        label_priority.update({i: l})\n",
        "\n",
        "    # Creamos el modelo y el tokenizador\n",
        "    tokenizer = AutoTokenizer.from_pretrained(pr_tokenizer_checkpoint)\n",
        "    if tokenizer.pad_token is None:\n",
        "        tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n",
        "\n",
        "    model = AutoModelForSequenceClassification.from_pretrained(\n",
        "        pr_model_checkpoint, num_labels=len(priority_label),\n",
        "        id2label=label_priority, label2id=priority_label\n",
        "        )\n",
        "    def preprocess_function(examples):\n",
        "        # Tokenizamos las entradas\n",
        "        max_input_length = 1024\n",
        "        max_target_length = 128\n",
        "        model_inputs = tokenizer(examples['description'],\n",
        "                                 max_length=max_input_length,\n",
        "                                 truncation=True,\n",
        "                                 padding=True,\n",
        "                                 return_tensors=\"pt\")\n",
        "        # Tokenizamos las etiquetas\n",
        "        labels = [priority_label[priority] for priority in examples['priority']]\n",
        "        # Asignamos los tokens como entrada\n",
        "        model_inputs[\"labels\"] = labels\n",
        "        return model_inputs\n",
        "\n",
        "    tokenized_dataset = dataset.map(\n",
        "        preprocess_function,\n",
        "        batched=True,\n",
        "        num_proc = 4,\n",
        "        remove_columns = list(dataset[\"train\"].to_dict().keys())\n",
        "    )\n",
        "\n",
        "    #Sacamos los pesos (para corregir sesgos de clasificación)\n",
        "    def get_weights(dataset):\n",
        "        # Transformamos el dataset a un dataframe y agrupamos las etiquetas\n",
        "        dataset = concatenate_datasets([dataset[\"train\"],dataset[\"validation\"],dataset[\"test\"]])\n",
        "        df = dataset.to_pandas()\n",
        "        #Si falta una etiqueta, lo añadimos y ponemos 0 (porque no hay etiquetas)\n",
        "        label_counts = df[\"labels\"].value_counts(normalize=True)\n",
        "        print(label_counts)\n",
        "        for i in range(len(priority_label)):\n",
        "            if i not in label_counts.index:\n",
        "                label_counts[i] = 0\n",
        "        # Calculamos los pesos como la inversa de la frecuencia de ocurrencias\n",
        "        class_weights = (1-label_counts.sort_index()).values\n",
        "        print(\"label weights:\", class_weights)\n",
        "        class_weights = from_numpy(class_weights).float().to(\"cuda\")\n",
        "        return class_weights\n",
        "\n",
        "    class_weights = get_weights(tokenized_dataset)\n",
        "    print(class_weights)\n",
        "\n",
        "    def compute_metrics(eval_pred):\n",
        "        metric = load('accuracy')\n",
        "        predictions, labels = eval_pred\n",
        "        predictions = np.argmax(predictions, axis=1)\n",
        "        return metric.compute(predictions=predictions, references=labels)\n",
        "\n",
        "    model_name = pr_model_checkpoint.split(\"/\")[-1]\n",
        "    batch_size = 16\n",
        "    training_args = TrainingArguments(\n",
        "        output_dir= model_name+\"-priority-classifier\",\n",
        "        learning_rate = 2e-5,\n",
        "        per_device_train_batch_size = batch_size,\n",
        "        per_device_eval_batch_size = batch_size,\n",
        "        num_train_epochs = 2,\n",
        "        weight_decay = 0.01,\n",
        "        eval_strategy = \"epoch\",\n",
        "        save_strategy = \"epoch\",\n",
        "    )\n",
        "\n",
        "    #Creamos el agrupador de datos\n",
        "    data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
        "\n",
        "    #Definimos un entrenador personalizado que tiene en cuenta los pesos\n",
        "    class WeightedLossTrainer(Trainer):\n",
        "        def compute_loss(self, model, inputs, return_outputs=False):\n",
        "            # Añadimos las entradas al modelo y extraemos los logits\n",
        "            outputs = model(**inputs)\n",
        "            logits = outputs.get(\"logits\")\n",
        "            # Extraemos las etiquetas\n",
        "            labels = inputs.get(\"labels\")\n",
        "            # Definimos la función de perdida con pesos de sesgo\n",
        "            loss_func=nn.CrossEntropyLoss (weight=class_weights)\n",
        "            # Calculamos pérdidas\n",
        "            loss = loss_func(logits, labels)\n",
        "            return (loss, outputs) if return_outputs else loss\n",
        "\n",
        "    trainer =  WeightedLossTrainer(\n",
        "                model=model,\n",
        "                args=training_args,\n",
        "                train_dataset=tokenized_dataset[\"train\"],\n",
        "                eval_dataset=tokenized_dataset[\"validation\"],\n",
        "                tokenizer=tokenizer,\n",
        "                data_collator=data_collator,\n",
        "                compute_metrics=compute_metrics)\n",
        "\n",
        "    trainer.train()\n",
        "    return trainer\n",
        "\n",
        "print(\"[*] Creating priority classifier model...\")\n",
        "rule_priority = train_priority(dataset_split)\n",
        "rule_priority.save_model()\n",
        "\n",
        "print(\"[+] Model has been created and saved!\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ritQ-Q3OHwQC"
      },
      "source": [
        "_Evaluamos el consumo y liberamos memoria_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NTmerJFfHsSp",
        "outputId": "36799853-896e-4d07-dab5-fed23942467b"
      },
      "outputs": [],
      "source": [
        "print_usage()\n",
        "del rule_priority\n",
        "cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CtwEXSsBm9Ih"
      },
      "source": [
        "### Fase 3: Prueba y validación\n",
        "\n",
        "Una vez entrenado los modelos, generaremos una regla y validaremos si es una regla Falco válida y si se ajusta a la descripción de entrada"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nW6PsiiG2lW1"
      },
      "source": [
        "#### Validación manual\n",
        "Se genera un texto de prueba que puede ser evaluado a mano"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3RsO6EHum9Ii",
        "outputId": "e5a7dc83-1504-4b38-d6f4-05ae10281b39"
      },
      "outputs": [],
      "source": [
        "#@title\n",
        "from transformers import pipeline, logging\n",
        "\n",
        "#Callamos las advertencias de transformers menos críticas\n",
        "logging.set_verbosity_error()\n",
        "\n",
        "# Descripción de una detección de la técnica T1098.002\n",
        "# Esta descripción no está en el dataset de reglas.\n",
        "# Obtenido de MITRE ATT&CK.\n",
        "test = \"Monitor for unusual Exchange and Office 365 \"+\\\n",
        "        \"email account permissions changes that may indicate \"+\\\n",
        "        \"excessively broad permissions (including memberships \"+\\\n",
        "        \"in privileged groups) being granted to compromised accounts.\"\n",
        "\n",
        "########### VERSIÓN 1 ###########\n",
        "\n",
        "#Cargamos el modelo\n",
        "#@markdown #Variables de la Versión 1\n",
        "v1_model_name = \"t5-small-rulegen\" #@param  {type:\"string\"}\n",
        "condition_model_name = v1_model_name+\"-v1-condition\"\n",
        "output_model_name = v1_model_name+\"-v1-output\"\n",
        "v1_tokenizer_checkpoint = \"google-t5/t5-small\" #@param  {type:\"string\"}\n",
        "condition_pipe =  pipeline(\"text2text-generation\",\n",
        "                           model=condition_model_name,\n",
        "                           tokenizer=v1_tokenizer_checkpoint)\n",
        "output_pipe =  pipeline(\"text2text-generation\",\n",
        "                        model=output_model_name,\n",
        "                        tokenizer=v1_tokenizer_checkpoint)\n",
        "\n",
        "print(\"-------------\")\n",
        "print(\"VERSION 1:\")\n",
        "print(condition_pipe(test))\n",
        "print(output_pipe(test))\n",
        "print(\"-------------\")\n",
        "\n",
        "########### VERSIÓN 2 ###########\n",
        "#@markdown ------\n",
        "#@markdown #Variables de la Versiones 2 y 3\n",
        "#Cargamos el modelo\n",
        "v2_model_name = \"gpt2\" #@param  {type:\"string\"}\n",
        "condition_model_name = v2_model_name+\"-rulegen-v2-condition\"\n",
        "output_model_name = v2_model_name+\"-rulegen-v2-output\"\n",
        "v2_tokenizer_checkpoint = \"sgugger/gpt2-like-tokenizer\" #@param  {type:\"string\"}\n",
        "condition_pipe =  pipeline(\"text-generation\",\n",
        "                           model=condition_model_name,\n",
        "                           tokenizer=v2_tokenizer_checkpoint,\n",
        "                           device = 0)\n",
        "output_pipe =  pipeline(\"text-generation\",\n",
        "                        model=output_model_name,\n",
        "                        tokenizer=v2_tokenizer_checkpoint,\n",
        "                        device = 0)\n",
        "\n",
        "print(\"-------------\")\n",
        "print(\"VERSION 2:\")\n",
        "print(condition_pipe(test+\":\"))\n",
        "print(output_pipe(test+\":\"))\n",
        "print(\"-------------\")\n",
        "\n",
        "########### VERSIÓN 3 ###########\n",
        "model_name = v2_model_name+\"-rulegen-v3\"\n",
        "pipe = pipeline(\"text-generation\",\n",
        "                model=model_name,\n",
        "                tokenizer=v2_tokenizer_checkpoint,\n",
        "                device = 0)\n",
        "nom_pipe = pipeline(\"summarization\", model=\"t5-small\")\n",
        "name = nom_pipe(test, min_length = 5, max_length=10)[0][\"summary_text\"]\n",
        "print(\"-------------\")\n",
        "print(\"VERSION 3:\")\n",
        "print(pipe(\"rule: \"+name+\"\\n  desc: \"+test+\"\\n  condition: \", max_length = 1000))\n",
        "print(\"-------------\")\n",
        "\n",
        "########### GENERADOR LLM ###########\n",
        "rule, price = gpt_generate_rule(test)\n",
        "print(\"-------------\")\n",
        "print(\"Rule generated by GPT-4o:\\n\"+rule)\n",
        "print(\"-------------\")\n",
        "print(\"Price:\",str(round(price,3))+\"$\")\n",
        "\n",
        "########### CLASIFICADOR DE PRIORIDAD ###########\n",
        "#@markdown ------\n",
        "#@markdown #Parámetros del modelo clasificador\n",
        "#@markdown _Nota: el tokenizador es el mismo que el nombre del modelo_\n",
        "pri_model_name = \"distilbert-base-uncased\" #@param {type:\"string\"}\n",
        "model_name = pri_model_name+\"-priority-classifier\"\n",
        "pipe = pipeline(\"text-classification\",\n",
        "                model=model_name,\n",
        "                tokenizer=pri_model_name)\n",
        "print(\"-------------\")\n",
        "print(\"Text classification model:\")\n",
        "print(pipe(test))\n",
        "print(\"-------------\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JAyhbgt627Q9"
      },
      "source": [
        "#### Pruebas automáticas\n",
        "Usaremos el dataset de prueba y medimos la tasa de éxito, que es igual a la razón del número de reglas satisfactorias entre el tamaño de la muestra."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vybNTL9fdvLt"
      },
      "source": [
        "##### Función de validación de sintaxis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k6eFXZFA3hP8",
        "outputId": "e061c4e0-00d1-4829-9222-85e6ed1db651"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "\n",
        "# Prueba de sintaxis\n",
        "def check_syntax(condition):\n",
        "    # Tokenizamos la condición\n",
        "    tokens = list(filter(None, re.split(r'\\s|([^\\w@#])', condition)))\n",
        "\n",
        "    # Verificar paréntesis emparejados\n",
        "    def check_parentheses(tokens):\n",
        "        stack = []\n",
        "        for token in tokens:\n",
        "            if token == '(':\n",
        "                stack.append(token)\n",
        "            elif token == ')':\n",
        "                if not stack:\n",
        "                    return False\n",
        "                stack.pop()\n",
        "        return not stack\n",
        "\n",
        "    if not check_parentheses(tokens):\n",
        "        return False\n",
        "\n",
        "    # Definir operadores y funciones válidas\n",
        "    bin_operators = {\"=\", \"!=\", \"<=\", \">=\", \">\",\n",
        "                           \"contains\", \"icontains\", \"startswith\",\"endswith\",\n",
        "                            \"glob\", \"in\", \"and\", \"or\", \"intersects\", \"pmatch\",\n",
        "                            \"exists\", \"bcontains\", \"bstartswith\"}\n",
        "    uni_operators = {\"val\", \"tolower\", \"toupper\"}\n",
        "    i = 0\n",
        "    n = len(tokens)\n",
        "    while i < n:\n",
        "        token = tokens[i]\n",
        "        # Verificar operadores binarios\n",
        "        if token in bin_operators:\n",
        "            if token == \"glob\":\n",
        "                if i + 1 >= n or tokens[i + 1] != '\"':\n",
        "                    return False\n",
        "                i += 2\n",
        "            elif (i == 0 or\n",
        "                  i == n - 1 or\n",
        "                  tokens[i - 1] in bin_operators or\n",
        "                  tokens[i + 1] in bin_operators):\n",
        "                pass  # Operadores binarios lógicos sin valor a la derecha están permitidos\n",
        "            else:\n",
        "                i += 1\n",
        "        # Verificar operadores unitarios\n",
        "        elif token in uni_operators:\n",
        "            if i + 1 >= n or tokens[i + 1] != '(':\n",
        "                return False\n",
        "            j = i + 2\n",
        "            while j < n and tokens[j] != ')':\n",
        "                j += 1\n",
        "            if j >= n:\n",
        "                return False\n",
        "            i = j\n",
        "        # Verificar conjuntos entre paréntesis\n",
        "        elif token == '(':\n",
        "            j = i + 1\n",
        "            while j < n and tokens[j] != ')':\n",
        "                if tokens[j] == ',' and (j == i + 1 or\n",
        "                                         j == n - 1 or\n",
        "                                         tokens[j + 1] == ',' or\n",
        "                                         tokens[j - 1] == ','):\n",
        "                    return False\n",
        "                j += 1\n",
        "            if j >= n:\n",
        "                return False\n",
        "            i = j\n",
        "\n",
        "        i += 1\n",
        "    return True\n",
        "\n",
        "condition = 'tolower(val(name)) contains john and age >='\n",
        "result = check_syntax(condition)\n",
        "print(result)  # True means correct, False means invalid"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n0ooXpG4d6J_"
      },
      "source": [
        "##### Modelo calculador de afinidad semántica"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5lahLbdvIMXK"
      },
      "source": [
        "_Creamos el modelo de semantica:_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MOFVDccjIQly",
        "outputId": "2f5a985a-baab-4df6-f7f7-ea3d1b5cca80"
      },
      "outputs": [],
      "source": [
        "#Usando la versión 1 para la tarea de resumen,\n",
        "#creamos un modelo que resuma una regla y a\n",
        "#partir del resumen evaluamos la similitud\n",
        "from torch import no_grad, cuda\n",
        "from transformers import AutoModel, logging\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from datasets import load_from_disk\n",
        "\n",
        "#Callamos las advertencias de transformers menos críticas\n",
        "logging.set_verbosity_error()\n",
        "\n",
        "# Generamos el modelo extractor de semántica\n",
        "dataset = load_from_disk(\"files/datasets/dataset_split\")\n",
        "\n",
        "model_checkpoint = \"google-t5/t5-small\" #@param  {type:\"string\"}\n",
        "tokenizer_checkpoint = \"google-t5/t5-small\" #@param  {type:\"string\"}\n",
        "\n",
        "print(\"Initiating training script...\")\n",
        "sem_extractor_trainer = train_v1(dataset,\n",
        "                        model_checkpoint,\n",
        "                        tokenizer_checkpoint,\n",
        "                        \"condition\",\n",
        "                        \"description\",\n",
        "                        \"sem-extractor\")\n",
        "sem_extractor_trainer.save_model()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-8saaQ0BIQ-i"
      },
      "source": [
        "_Evaluamos el consumo y liberamos memoria:_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vTh_YY9kIWWS",
        "outputId": "7a724a44-fb62-44bb-dfca-a5e9013742ee"
      },
      "outputs": [],
      "source": [
        "print_usage()\n",
        "del sem_extractor_trainer\n",
        "cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1TsbHvVdIXRn"
      },
      "source": [
        "_Declaramos las funciones de sintaxis y de similitud:_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "code",
        "id": "ZpUumW24eAZ1"
      },
      "outputs": [],
      "source": [
        "# Función para calcular la similitud coseno entre dos textos\n",
        "def check_similarity(text1, text2):\n",
        "        # Crear un vectorizador de conteo de palabras\n",
        "        vectorizer = CountVectorizer().fit([text1, text2])\n",
        "\n",
        "        # Transformar los textos en vectores de conteo\n",
        "        vectors = vectorizer.transform([text1, text2])\n",
        "\n",
        "        # Calcular la similaridad del coseno entre los dos vectores\n",
        "        similarity = cosine_similarity(vectors)[0][1]\n",
        "        return similarity\n",
        "\n",
        "# Definimos función de similaridad\n",
        "def check_semantic(condition: str, description: str):\n",
        "    model_name = \"./\"+model_checkpoint.split(\"/\")[-1]+\"-rulegen-sem-extractor\"\n",
        "\n",
        "    #Cargamos el modelo y el tokenizador\n",
        "    summarizer = AutoModel.from_pretrained(model_name)\n",
        "    summarizer = pipeline(\"text2text-generation\",\n",
        "                        model=model_name,\n",
        "                        tokenizer=tokenizer_checkpoint,\n",
        "                        device = 0)\n",
        "\n",
        "    #Generamos el resumen de la condición\n",
        "    res = summarizer(condition,\n",
        "                     min_length = int(len(description)*0.6),\n",
        "                     max_length = len(description))[-1][\"generated_text\"]\n",
        "\n",
        "\n",
        "    return check_similarity(res,description)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3uM2pSvMjifS"
      },
      "source": [
        "##### Ejecución de funciones"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wjJi6t95joiw",
        "outputId": "3c7c3a34-172d-4345-baf0-5fcaa4c44290"
      },
      "outputs": [],
      "source": [
        "#@title\n",
        "from datasets import load_from_disk\n",
        "from transformers import pipeline, logging\n",
        "import yaml\n",
        "\n",
        "#Silenciamos las advertencias de transformers menos críticas\n",
        "logging.set_verbosity_error()\n",
        "\n",
        "# Ejecutamos las funciones declaradas para realizar las pruebas\n",
        "# Cargamos el dataset de prueba\n",
        "test_dataset = load_from_disk(\"files/datasets/dataset_split\")[\"test\"]\n",
        "\n",
        "# Para almacenar los costes\n",
        "prices = []\n",
        "\n",
        "############## VARIABLES ##############\n",
        "v1_model_name = \"t5-small-rulegen\"\n",
        "v1_tokenizer_checkpoint = \"google-t5/t5-small\"\n",
        "v2_model_name = \"gpt2\"\n",
        "v2_tokenizer_checkpoint = \"sgugger/gpt2-like-tokenizer\"\n",
        "\n",
        "#######################################\n",
        "\n",
        "# Generador de reglas\n",
        "def generate_rule(description: str, version: str):\n",
        "\n",
        "    # Creamos las variables\n",
        "    if version == 'v1':\n",
        "        condition_model_name = \"./\"+v1_model_name.split(\"/\")[-1]+\"-v1-condition\"\n",
        "\n",
        "        condition_pipe =  pipeline(\"text2text-generation\",\n",
        "                                   model=condition_model_name,\n",
        "                                   tokenizer=v1_tokenizer_checkpoint,\n",
        "                                   device = 0)\n",
        "\n",
        "    elif version == 'v2':\n",
        "        condition_model_name = \"./\"+v2_model_name.split(\"/\")[-1]+\"-rulegen-v2-condition\"\n",
        "        condition_pipe =  pipeline(\"text-generation\",\n",
        "                                   model=condition_model_name,\n",
        "                                   tokenizer=v2_tokenizer_checkpoint,\n",
        "                                   device = 0)\n",
        "\n",
        "    elif version == 'v3':\n",
        "        model_name = \"./\"+v2_model_name.split(\"/\")[-1]+\"-rulegen-v3\"\n",
        "        pipe = pipeline(\"text-generation\",\n",
        "                        model=model_name,\n",
        "                        tokenizer=v2_tokenizer_checkpoint,\n",
        "                        device = 0)\n",
        "\n",
        "    # Generamos y devolvemos la regla\n",
        "    if version == 'v1' or version == 'v2':\n",
        "        # La versión 2 usa un separador que indica cuando\n",
        "        # empieza la generación de la condición/salida\n",
        "        size = 20\n",
        "        if version == 'v2':\n",
        "            sep = \":\"\n",
        "            size += len(description+sep)\n",
        "        else:\n",
        "            sep = \"\"\n",
        "\n",
        "        condition = condition_pipe(description+sep, max_length = size)[-1][\"generated_text\"]\n",
        "        if sep != \"\":\n",
        "            condition = condition.split(sep)[-1]\n",
        "\n",
        "        # Evaluamos consumo y limpiamos la memoria\n",
        "        print_usage()\n",
        "        del condition_pipe\n",
        "        cuda.empty_cache()\n",
        "\n",
        "        return condition\n",
        "\n",
        "    elif version == 'v3':\n",
        "        # Versión 3 devuelve una regla completa\n",
        "        generated_rule = pipe(\"desc: \"+description+\"\\n  condition: \", max_length=1000)[-1][\"generated_text\"]\n",
        "        # Evaluamos consumo y limpiamos la memoria\n",
        "        print_usage()\n",
        "        del pipe\n",
        "        cuda.empty_cache()\n",
        "\n",
        "        try:\n",
        "            rule = yaml.safe_load(generated_rule)\n",
        "            return rule[0]['condition'] if type(rule) == list else rule['condition']\n",
        "        except:\n",
        "            return \"None)\"\n",
        "\n",
        "    elif version == 'llm':\n",
        "        try:\n",
        "            output, price = gpt_generate_rule(description)\n",
        "            prices.append(price)\n",
        "            print(\"Prices:\",prices)\n",
        "            rule = yaml.safe_load(output)\n",
        "            return rule[0]['condition'] if type(rule) == list else rule['condition']\n",
        "        except:\n",
        "            return \"None)\"\n",
        "\n",
        "# Realizamos las pruebas\n",
        "results = {\n",
        "    'v1': {'syntax':None, 'similarity': None, 'semantic': None},\n",
        "    'v2': {'syntax':None, 'similarity': None, 'semantic': None},\n",
        "    'v3': {'syntax':None, 'similarity': None, 'semantic': None},\n",
        "    'llm': {'syntax':None, 'similarity': None, 'semantic': None, 'avg_price':None}\n",
        "}\n",
        "\n",
        "\n",
        "for version in results.keys():\n",
        "    syntax_res = 0   #Nº de reglas cuya sintaxis es correcta\n",
        "    semantic_res = []    #Afinidad semántica de las reglas\n",
        "    similarity_res = []  #Similitud de las reglas con las originales\n",
        "    for description, condition in zip(test_dataset['description'], test_dataset['condition']):\n",
        "        generated_condition = generate_rule(description, version)\n",
        "        syntax_res += 1 if check_syntax(generated_condition) else 0\n",
        "        semantic_res.append(check_semantic(generated_condition, description))\n",
        "        similarity_res.append(check_similarity(generated_condition, condition))\n",
        "\n",
        "    # Calculamos el ratio de satisfacción sintáctica, semántica y de similitud\n",
        "    dataset_size = len(test_dataset)\n",
        "    #print(f\"----------\\nsize: {dataset_size}\\nsimilarity_hits: {similarity_res}\\nsemantic_hits: {semantic_success}\\nsyntax_hits:{syntax_success}\")\n",
        "    results[version]['syntax'] = round(syntax_res/dataset_size,4)\n",
        "    results[version]['semantic'] = round(sum(semantic_res)/len(semantic_res),4)\n",
        "    results[version]['similarity'] = round(sum(similarity_res)/len(similarity_res),4)\n",
        "    if version == 'llm':\n",
        "        results[version]['avg_price'] = round(sum(prices)/len(prices),3)\n",
        "\n",
        "# Imprimimos los resultados\n",
        "print(\"====================================\")\n",
        "print(\"ATTENTION: the rules have NOT been expanded\")\n",
        "for version in results.keys():\n",
        "    print(f\"{version}: {results[version]}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LNbeIQD4pv6c"
      },
      "source": [
        "### Aplicación final\n",
        "Aquí definimos las funciones que permiten usar los modelos afinados y los modelos pre-entrenados mediante prompt."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bNezuIANpuGV",
        "outputId": "17afe09d-2a81-40dc-90f6-3061b29b26a8"
      },
      "outputs": [],
      "source": [
        "#@title\n",
        "import yaml\n",
        "from transformers import pipeline\n",
        "#Callamos las advertencias de transformers menos críticas\n",
        "#logging.set_verbosity_error()\n",
        "\n",
        "########### VARIABLES ###########\n",
        "#@markdown ### Elegir versión e indicar descripción\n",
        "version       = \"llm\" #@param [\"v1\",\"v2\",\"v3\",\"llm\"]\n",
        "description   = \"Monitor for unusual Exchange and Office 365 email account permissions changes that may indicate excessively broad permissions (including memberships in privileged groups) being granted to compromised accounts.\" #@param {type: \"string\"}\n",
        "#@markdown ----------------------\n",
        "#@markdown ###Variables (rellenarlas todas. V2 y V3 usan las mismas)\n",
        "v1_model_name = \"t5-small\" #@param {type: \"string\"}\n",
        "v1_tokenizer  = \"t5-small\" #@param {type: \"string\"}\n",
        "v2_model_name = \"gpt2\" #@param {type: \"string\"}\n",
        "v2_tokenizer  = \"sgugger/gpt2-like-tokenizer\" #@param {type: \"string\"}\n",
        "pr_model_name = \"distilbert-base-uncased\" #@param {type: \"string\"}\n",
        "pr_tokenizer  = \"distilbert-base-uncased\" #@param {type: \"string\"}\n",
        "\n",
        "#################################\n",
        "\n",
        "# Función generador de reglas\n",
        "def gen_rule(description: str, version: str) -> str:\n",
        "    if description != \"\":\n",
        "        # VERSIÓN 1\n",
        "        if version == 'v1' and\\\n",
        "        v1_model_name != \"\" and\\\n",
        "        pr_model_name != \"\" and\\\n",
        "        v1_tokenizer  != \"\":\n",
        "            pipe_gen = pipeline(\"text2text-generation\",\n",
        "                                model = \"./\"+v1_model_name.split(\"/\")[-1]+\"-rulegen-v1-condition\",\n",
        "                                tokenizer = v1_tokenizer,\n",
        "                                device = 0)\n",
        "            pipe_out = pipeline(\"text2text-generation\",\n",
        "                                model = \"./\"+v1_model_name.split(\"/\")[-1]+\"-rulegen-v1-output\",\n",
        "                                tokenizer = v1_tokenizer,\n",
        "                                device = 0)\n",
        "            pipe_pri = pipeline(\"text-classification\",\n",
        "                                model = \"./\"+pr_model_name.split(\"/\")[-1]+\"-priority-classifier\",\n",
        "                                tokenizer = pr_tokenizer,\n",
        "                                device = 0)\n",
        "            pipe_nom = pipeline(\"summarization\",\n",
        "                                model = v1_model_name,\n",
        "                                device = 0)\n",
        "            res = {\"rule\": pipe_nom(description,\n",
        "                                    min_length = 5,\n",
        "                                    max_length = 10)[-1][\"summary_text\"],\n",
        "                   \"desc\": description,\n",
        "                   \"condition\": pipe_gen(description, min_length = 50)[-1][\"generated_text\"],\n",
        "                   \"output\": pipe_out(description, min_length = 50)[-1][\"generated_text\"],\n",
        "                   \"priority\":pipe_pri(description)[-1][\"label\"],\n",
        "                   }\n",
        "            print_usage()\n",
        "            del pipe_gen, pipe_nom, pipe_out, pipe_pri\n",
        "            cuda.empty_cache()\n",
        "            return yaml.safe_dump(res, sort_keys=False).strip().replace(\"[]\",\"\")+\"\\n\"\n",
        "\n",
        "        # VERSIÓN 2\n",
        "        elif version == 'v2' and\\\n",
        "        v2_model_name != \"\" and\\\n",
        "        pr_model_name != \"\" and\\\n",
        "        v2_tokenizer  != \"\":\n",
        "            pipe_gen = pipeline(\"text-generation\",\n",
        "                                model = \"./\"+v2_model_name.split(\"/\")[-1]+\"-rulegen-v2-condition\",\n",
        "                                tokenizer = v2_tokenizer)\n",
        "            pipe_out = pipeline(\"text-generation\",\n",
        "                                model = \"./\"+v2_model_name.split(\"/\")[-1]+\"-rulegen-v2-output\",\n",
        "                                tokenizer = v2_tokenizer)\n",
        "            pipe_pri = pipeline(\"text-classification\",\n",
        "                                model = \"./\"+pr_model_name.split(\"/\")[-1]+\"-priority-classifier\",\n",
        "                                tokenizer = pr_tokenizer)\n",
        "            pipe_nom = pipeline(\"summarization\",\n",
        "                                model = v2_model_name)\n",
        "            res = {\"rule\": pipe_nom(description,\n",
        "                                    min_length = len(description),\n",
        "                                    max_length = len(description)+10)[-1][\"summary_text\"],\n",
        "                   \"desc\": description,\n",
        "                   \"condition\": pipe_gen(description+\":\", min_length = 50)[-1][\"generated_text\"].split(description)[-1],\n",
        "                   \"output\": pipe_out(description+\":\", min_length = 50)[-1][\"generated_text\"].split(description)[-1],\n",
        "                   \"priority\":pipe_pri(description)[-1][\"label\"],\n",
        "                   }\n",
        "            print_usage()\n",
        "            del pipe_gen, pipe_nom, pipe_out, pipe_pri\n",
        "            cuda.empty_cache()\n",
        "            return yaml.safe_dump(res, sort_keys=False).strip().replace(\"[]\",\"\")+\"\\n\"\n",
        "\n",
        "        # VERSIÓN 3\n",
        "        elif version == 'v3' and v2_model_name != \"\" and v2_tokenizer  != \"\":\n",
        "            pipe = pipeline(\"text-generation\",\n",
        "                            model = \"./\"+v2_model_name.split(\"/\")[-1]+\"-rulegen-v3\",\n",
        "                            tokenizer = v2_tokenizer)\n",
        "            pipe_nom = pipeline(\"text-generation\",\n",
        "                                model = v2_model_name)\n",
        "            name = pipe_nom(description+\"title: \", max_new_tokens = 10)[0][\"generated_text\"].split(description+\"title: \")[-1]\n",
        "            print_usage()\n",
        "            del pipe, pipe_nom\n",
        "            cuda.empty_cache()\n",
        "            return pipe(\"description:\"+description+\"\\n  condition:\")\n",
        "\n",
        "        # GENERADOR LLM\n",
        "        elif version == 'llm':\n",
        "            return gpt_generate_rule(description)\n",
        "\n",
        "        # Gestión de errores\n",
        "        else:\n",
        "            if version not in [\"v1\",\"v2\",\"v3\",\"llm\"]:\n",
        "                raise Exception(\"Wrong version specified\")\n",
        "            else:\n",
        "                Exception(\"Some parameters are empty, please fulfill them.\")\n",
        "    else:\n",
        "        Exception(\"The description is empty, please fulfill it.\")\n",
        "\n",
        "\n",
        "\n",
        "print(\"The generated rule is:\",gen_rule(description, version),sep=\"\\n\")"
      ]
    },
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      "metadata": {},
      "source": [
        "### Creditos\n",
        "\n",
        "*©2023 Carel Sánchez*\n",
        "\n",
        "*Algunos derechos reservados*\n",
        "\n",
        "*Este documento se distribuye bajo la licencia \"Atribución 4.0 Internacional\" de Creative Commons*"
      ]
    }
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