similarity_NIST.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fegxPFvil9yJ"
      },
      "source": [
        "https://nvlpubs.nist.gov/nistpubs/CSWP/NIST.CSWP.04162018.pdf\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JhAP2cDQoreg"
      },
      "outputs": [],
      "source": [
        "#approach is based on: BERT based embeddings (Transformers) + Cosine Similarity\n",
        "#inspired by: https://towardsdatascience.com/semantic-similarity-using-transformers-8f3cb5bf66d6\n",
        "#why i have choosen this approach: https://medium.com/@adriensieg/text-similarities-da019229c894\n",
        "\n",
        "# Install a pip package in the current Jupyter kernel\n",
        "import sys\n",
        "!{sys.executable} -m pip install transformers\n",
        "!{sys.executable} -m pip install sentence-transformers\n",
        "\n",
        "from sentence_transformers import SentenceTransformer, util\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9Dr1sh_mo04P"
      },
      "outputs": [],
      "source": [
        "# List of models optimized for semantic textual similarity can be found at:\n",
        "# https://docs.google.com/spreadsheets/d/14QplCdTCDwEmTqrn1LH4yrbKvdogK4oQvYO1K1aPR5M/edit#gid=0\n",
        "model = SentenceTransformer('stsb-mpnet-base-v2')\n",
        "\n",
        "#import the mitreattack-python library: https://mitreattack-python.readthedocs.io/en/latest/index.html\n",
        "#documentation for the API: https://mitreattack-python.readthedocs.io/en/latest/mitre_attack_data/mitre_attack_data.html#api-reference"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "nJ4z8_89o8d8"
      },
      "outputs": [],
      "source": [
        "from ctypes import sizeof\n",
        "import json\n",
        "import csv\n",
        "\n",
        "\n",
        "#define the categories of the NIST Framework for Improving Critical Infrastructure Cybersecurity\n",
        "categories = {\n",
        "    \"Asset Management\": \"The data, personnel, devices, systems, and facilities that enable the organization to achieve business purposes are identified and managed consistent with their relative importance to organizational objectives and the organization’s risk strategy.\",\n",
        "    \"Business Environment\": \"The organization’s mission, objectives, stakeholders, and activities are understood and prioritized; this information is used to inform cybersecurity roles, responsibilities, and risk management decisions.\",\n",
        "    \"Governance\": \"The policies, procedures, and processes to manage and monitor the organization’s regulatory, legal, risk, environmental, and operational requirements are understood and inform the management of cybersecurity risk.\",\n",
        "    \"Risk Assessment\": \"The organization understands the cybersecurity risk to organizational operations (including mission, functions, image, or reputation), organizational assets, and individuals.\",\n",
        "    \"Risk Management Strategy\": \"The organization’s priorities, constraints, risk tolerances, and assumptions are established and used to support operational risk decisions.\",\n",
        "    \"Supply Chain Risk Management\": \"The organization’s priorities, constraints, risk tolerances, and assumptions are established and used to support risk decisions associated with managing supply chain risks.\",\n",
        "    \"Identity Management and Access Control\": \"Access to physical and logical assets and associated facilities is limited to authorized users, processes, and devices, and is managed consistent with the assessed risk of unauthorized access to authorized activities and transactions.\",\n",
        "    \"Awareness and Training\": \"The organization’s personnel and partners are provided cybersecurity awareness education and are trained to perform their cybersecurityrelated duties and responsibilities consistent with related policies, procedures, and agreements.\",\n",
        "    \"Data Security\": \"Information and records (data) are managed consistent with the organization’s risk strategy to protect the confidentiality, integrity, and availability of information.\",\n",
        "    \"Information Protection Processes and Procedures\": \"Security policies (that address purpose, scope, roles, responsibilities, management commitment, and coordination among organizational entities), processes, and procedures are maintained and used to manage protection of information systems and assets.\",\n",
        "    \"Maintenance\": \"Maintenance and repairs of industrial control and information system components are performed consistent with policies and procedures.\",\n",
        "    \"Protective Technology\": \"Technical security solutions are managed to ensure the security and resilience of systems and assets, consistent with related policies, procedures, and agreements.\",\n",
        "    \"Anomalies and Events\": \"Anomalous activity is detected and the potential impact of events is understood.\",\n",
        "    \"Security Continuous Monitoring\": \"The information system and assets are monitored at discrete intervals to identify cybersecurity events and verify the effectiveness of protective measures.\",\n",
        "    \"Detection Processes\": \"Detection processes and procedures are maintained and tested to ensure timely and adequate awareness of anomalous events.\",\n",
        "    \"Response Planning\": \"Response processes and procedures are executed and maintained, to ensure timely response to detected cybersecurity events.\",\n",
        "    \"Communications (Respond)\": \"Response activities are coordinated with internal and external stakeholders (e.g. external support from law enforcement agencies).\",\n",
        "    \"Analysis\": \"Analysis is conducted to ensure adequate response and support recovery activities.\",\n",
        "    \"Mitigation\": \"Activities are performed to prevent expansion of an event, mitigate its effects, and resolve the incident.\",\n",
        "    \"Improvements (Respond)\": \"Organizational response activities are improved by incorporating lessons learned from current and previous detection/response activities.\",\n",
        "    \"Recovery Planning\": \"Recovery processes and procedures are executed and maintained to ensure timely restoration of systems or assets affected by cybersecurity incidents.\",\n",
        "    \"Improvements (Recover)\": \"Recovery planning and processes are improved by incorporating lessons learned into future activities.\",\n",
        "    \"Communications (Recover)\": \"Restoration activities are coordinated with internal and external parties (e.g. coordinating centers, Internet Service Providers, owners of attacking systems, victims, other CSIRTs, and vendors).\"\n",
        "}\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h9ds7aoNrE2n"
      },
      "outputs": [],
      "source": [
        "from sentence_transformers import SentenceTransformer, util\n",
        "import csv\n",
        "import numpy as np\n",
        "import re\n",
        "import string\n",
        "\n",
        "#for stopwords\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('omw-1.4')\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "t6WySTE5heoQ"
      },
      "outputs": [],
      "source": [
        "# Initialize lemmatizer\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "category_embeddings = {}\n",
        "for category, description in categories.items():\n",
        "\n",
        "    # Remove punctuation from the text\n",
        "    description = description.translate(str.maketrans('', '', string.punctuation))\n",
        "\n",
        "    # Convert the text to lowercase\n",
        "    description = description.lower()\n",
        "\n",
        "    # Tokenize text\n",
        "    text_tokens = word_tokenize(description)\n",
        "\n",
        "    # Remove stopwords and lemmatize tokens\n",
        "    tokens_without_sw = [lemmatizer.lemmatize(word) for word in text_tokens if not word in stopwords.words()]\n",
        "\n",
        "    # Join tokens back into a filtered sentence\n",
        "    filtered_sentence = (\" \").join(tokens_without_sw)\n",
        "\n",
        "    # Update technique description\n",
        "    description = filtered_sentence\n",
        "\n",
        "    embedding = model.encode(description, convert_to_tensor=True, normalize_embeddings=True)\n",
        "    category_embeddings[category] = embedding\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l22F1wvfsU1x"
      },
      "outputs": [],
      "source": [
        "#abstract\n",
        "#with open(r\"unique_talks_without_speakers.csv\", encoding='utf8') as csv_file:\n",
        "with open(r\"groundtruth.csv\", encoding='utf8') as csv_file:\n",
        "    csv_reader = csv.reader(csv_file, delimiter=';')\n",
        "    result = {}\n",
        "    count = 0\n",
        "    \n",
        "    for row in csv_reader:\n",
        "      #if \"advisen\".lower() in row[0].lower(): #only consider one specific conference\n",
        "      if True:\n",
        "        abstract = row[2]\n",
        "        if (len(abstract) < 20): #if smaller then the abstract is missing ... and then we don't count this entry\n",
        "          continue\n",
        "        #print(abstract)\n",
        "        \n",
        "        # Remove links from the text\n",
        "        abstract = re.sub(r'http\\S+', '', abstract)\n",
        "\n",
        "        # Remove punctuation from the text\n",
        "        abstract = abstract.translate(str.maketrans('', '', string.punctuation))\n",
        "\n",
        "        # Convert the text to lowercase\n",
        "        abstract = abstract.lower()\n",
        "\n",
        "        # Tokenize text\n",
        "        text_tokens = word_tokenize(abstract)\n",
        "\n",
        "        # Remove stopwords and lemmatize tokens\n",
        "        tokens_without_sw = [lemmatizer.lemmatize(word) for word in text_tokens if not word in stopwords.words()]\n",
        "\n",
        "        # Join tokens back into a filtered sentence\n",
        "        filtered_sentence = (\" \").join(tokens_without_sw)\n",
        "\n",
        "        # Update technique description\n",
        "        abstract = filtered_sentence\n",
        "        #print(abstract)\n",
        "        embedding1 = model.encode(abstract, convert_to_tensor=True, normalize_embeddings=True)\n",
        "\n",
        "        count += 1\n",
        "\n",
        "        for category, description in categories.items():\n",
        "\n",
        "          # encode technique to get their embeddings\n",
        "          embedding2 = category_embeddings[category]\n",
        "\n",
        "          # compute similarity scores of two embeddings\n",
        "          cosine_score = util.pytorch_cos_sim(embedding1, embedding2)\n",
        "\n",
        "          tmp = result.get(category, 0)\n",
        "          result[category] = tmp + cosine_score.item()\n",
        "          #print(cosine_score, \" \", tactic_name)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "gh526QKUW89_"
      },
      "outputs": [],
      "source": [
        "for res in result.keys():\n",
        "  #print(res, result[res]/count)\n",
        "  tmp = result.get(res, 0)\n",
        "  result[res] = tmp/count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "ytfx0KhYnDLX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a2a0851-d7a0-44b8-daa3-8c38e268b8c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                           Category  no classification\n",
            "0                                  Asset Management           0.408680\n",
            "1                              Business Environment           0.341461\n",
            "2                                        Governance           0.411037\n",
            "3                                   Risk Assessment           0.327877\n",
            "4                          Risk Management Strategy           0.299213\n",
            "5                      Supply Chain Risk Management           0.291918\n",
            "6            Identity Management and Access Control           0.395499\n",
            "7                            Awareness and Training           0.314965\n",
            "8                                     Data Security           0.317704\n",
            "9   Information Protection Processes and Procedures           0.392046\n",
            "10                                      Maintenance           0.272125\n",
            "11                            Protective Technology           0.363418\n",
            "12                             Anomalies and Events           0.233328\n",
            "13                   Security Continuous Monitoring           0.388677\n",
            "14                              Detection Processes           0.235358\n",
            "15                                Response Planning           0.283630\n",
            "16                         Communications (Respond)           0.258445\n",
            "17                                         Analysis           0.201569\n",
            "18                                       Mitigation           0.266624\n",
            "19                           Improvements (Respond)           0.299249\n",
            "20                                Recovery Planning           0.300767\n",
            "21                           Improvements (Recover)           0.263494\n",
            "22                         Communications (Recover)           0.343713\n"
          ]
        }
      ],
      "source": [
        "from pandas.core.apply import relabel_result\n",
        "import pandas as pd\n",
        "\n",
        "df = pd.DataFrame({'Category': result.keys(),'no classification': result.values()})\n",
        "\n",
        "print(df)\n",
        "df.to_csv('nist.csv', header=True, index=False, encoding='utf-8')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load the CSV file into a pandas DataFrame\n",
        "df = pd.read_csv(\"nist.csv\", delimiter=',')\n",
        "\n",
        "# Set the first column as the DataFrame index\n",
        "df = df.set_index(df.columns[0])\n",
        "\n",
        "# Create a heatmap using the remaining columns as the data\n",
        "sns.heatmap(df.iloc[:, 0:], cmap=\"YlGnBu\")\n",
        "#sns.heatmap(df.iloc[:, 1:])\n",
        "\n",
        "plt.savefig('similarity.png', dpi=400, bbox_inches='tight')#change dpi for image resolution\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "-Hgsu1JmQ2f3",
        "outputId": "d8343fef-f442-42ce-fca3-425868fcf2cd"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "method for evaluating the results of the chatgpt evaluation:\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "TJ7lhcd1Z7n8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open(r\"unique_talks_without_speakers.csv\", encoding='utf8') as csv_file:\n",
        "    csv_reader = csv.reader(csv_file, delimiter=';')\n",
        "\n",
        "    # Read the column headers from the first row of the CSV file\n",
        "    column_names = next(csv_reader)\n",
        "\n",
        "    # Count the number of 1s in each column\n",
        "    counts = [0] * 23  # Initialize the counts to zero for each column\n",
        "    for row in csv_reader:\n",
        "        for i, val in enumerate(row):\n",
        "            if val == \"1\":\n",
        "                counts[i] += 1\n",
        "\n",
        "    # Print the counts for each column\n",
        "    for i, count in enumerate(counts):\n",
        "        if i > 7 and i < 22:\n",
        "            print(f\"{column_names[i]}: {count/counts[22]}\")\n",
        "    print(f\"total rows processed: {counts[22]}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "12Of5yFraOV6",
        "outputId": "c94a9c4f-d579-47ec-cf46-0da0b315390f"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Credential Access: 0.23964497041420119\n",
            "Execution: 0.39644970414201186\n",
            "Impact: 0.22781065088757396\n",
            "Persistence: 0.22781065088757396\n",
            "Privilege Escalation: 0.15088757396449703\n",
            "Lateral Movement: 0.06804733727810651\n",
            "Defense Evasion: 0.6124260355029586\n",
            "Exfiltration: 0.16272189349112426\n",
            "Discovery: 0.5473372781065089\n",
            "Collection: 0.4911242603550296\n",
            "Resource Development: 0.005917159763313609\n",
            "Reconnaissance: 0.047337278106508875\n",
            "Command and Control: 0.15976331360946747\n",
            "Initial Access: 0.14792899408284024\n",
            "total rows processed: 338\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}