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llm-examples/docs/it-threat-detection.ipynb
James Briggs 7ee1293bf2 updates to threat detection notebooks
2023-07-23 11:16:12 +08:00

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"source": [
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pinecone-io/examples/blob/master/docs/it-threat-detection.ipynb) [![Open nbviewer](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/nbviewer-shield.svg)](https://nbviewer.org/github/pinecone-io/examples/blob/master/docs/it-threat-detection.ipynb)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "b4Yv1jeGywpL"
},
"source": [
"## IT Threat Detection With Similarity Search\n",
"\n",
"[![Open nbviewer](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/full-link.svg)](https://colab.research.google.com/github/pinecone-io/examples/blob/master/learn/security/it-threat-detection.ipynb)\n",
"\n",
"This notebook shows how to use Pinecone's similarity search as a service to build an application for detecting rare events. Such application is common in cyber-security and fraud detection domains wherein only a tiny fraction of the events are malicious.\n",
"\n",
"Here we will build a network intrusion detector. Network intrusion detection systems monitor incoming and outgoing network traffic flow, raising alarms whenever a threat is detected. Here we use a deep-learning model and similarity search in detecting and classifying network intrusion traffic.\n",
"\n",
"We will start by indexing a set of labeled traffic events in the form of vector embeddings. Each event is either benign or malicious. The vector embeddings are rich, mathematical representations of the network traffic events. It is making it possible to determine how similar the network events are to one another using similarity-search algorithms built into Pinecone. Here we will transform network traffic events into vectors using a deep learning model from recent academic work.\n",
"\n",
"\n",
"We will then take some new (unseen) network events and search through the index to find the most similar matches, along with their labels. In such a way, we will propagate the matched labels to classify the unseen events as benign or malicious. Mind that the intrusion detection task is a challenging classification task because malicious events are sporadic. The similarity search service helps us sift the most relevant historical labeled events. That way, we identify these rare events while keeping a low rate of false alarms.\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "1OUSClcPhU4j"
},
"source": [
"## Setting up Pinecone\n",
"\n",
"We will first install and initialize Pinecone. You can get your [API Key here](https://app.pinecone.io)."
]
},
{
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"google-colab 1.0.0 requires pandas==1.5.3, but you have pandas 2.0.3 which is incompatible.\n",
"pandas-gbq 0.17.9 requires pyarrow<10.0dev,>=3.0.0, but you have pyarrow 12.0.1 which is incompatible.\n",
"tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.19.6 which is incompatible.\n",
"tensorflow-datasets 4.9.2 requires protobuf>=3.20, but you have protobuf 3.19.6 which is incompatible.\n",
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"\u001b[0m"
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"source": [
"!pip install -qU \\\n",
" \"pinecone-client[grpc]\"==2.2.2 \\\n",
" pinecone-datasets=='0.5.0rc10'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_cGTuY8dywpV",
"outputId": "0799f827-8187-4011-e8ae-1a880bc534ff"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/pinecone/index.py:4: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
" from tqdm.autonotebook import tqdm\n"
]
}
],
"source": [
"import pinecone\n",
"import os\n",
"\n",
"# Load Pinecone API key\n",
"api_key = os.getenv('PINECONE_API_KEY') or 'PINECONE_API_KEY'\n",
"# Set Pinecone environment/region - find next to API key in console\n",
"env = os.getenv('PINECONE_ENVIRONMENT') or 'PINECONE_ENVIRONMENT'\n",
"\n",
"pinecone.init(api_key=api_key, environment=env)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "31jd7os7RAej",
"outputId": "a5657975-ba80-43da-8c51-89812cb75d22"
},
"outputs": [
{
"data": {
"text/plain": [
"['it-threats-fast']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#List all present indexes associated with your key, should be empty on the first run\n",
"pinecone.list_indexes()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "a7ysNAlrjD_k"
},
"source": [
"## Installing other dependencies"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "r3g-b61IywpQ",
"outputId": "f973ecaa-132c-4115-f865-483739e5bccb"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.8/10.8 MB\u001b[0m \u001b[31m41.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m438.7/438.7 kB\u001b[0m \u001b[31m23.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.9/4.9 MB\u001b[0m \u001b[31m42.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m781.3/781.3 kB\u001b[0m \u001b[31m26.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"pandas-gbq 0.17.9 requires pyarrow<10.0dev,>=3.0.0, but you have pyarrow 12.0.1 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0m"
]
}
],
"source": [
"!pip install -qU \\\n",
" python-dateutil \\\n",
" tensorflow==2.10 \\\n",
" scikit-learn==1.3.0 \\\n",
" matplotlib==3.7.1"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "mc4ERmwniO1H"
},
"source": [
"## Define a New Pinecone Index"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "9_TIgYxBywpV"
},
"outputs": [],
"source": [
"# Pick a name for the new service\n",
"index_name = 'it-threats-fast'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "j7scTXFrywpV"
},
"outputs": [],
"source": [
"# Make sure index with the same name does not exist\n",
"if index_name in pinecone.list_indexes():\n",
" pinecone.delete_index(index_name)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "wggPSrKtywpW"
},
"source": [
"**Create an index**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "q5Q42v4mywpW"
},
"outputs": [],
"source": [
"pinecone.create_index(name=index_name, dimension=128, metric='euclidean')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "3eXIyr2-ywpW"
},
"source": [
"**Connect to the index**\n",
"\n",
"We create an index object, a class instance of `pinecone.GRPCIndex`, which will be used to interact with the created index."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "tTlvU_jvywpW",
"scrolled": true
},
"outputs": [],
"source": [
"index = pinecone.GRPCIndex(index_name=index_name)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "IOP9jCo5ywpX"
},
"source": [
"## Upload\n",
"Here we load network events as precomputed vector embeddings from `pinecone-datasets` library, then upload them into Pinecone's vector index."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "PzkJJd8ZYTNM"
},
"source": [
"### Download Data"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "vuOLNAehYTNN"
},
"source": [
"**Download data for 22-02-2018 and 23-02-2018**\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "cGXf6PmhYTNR",
"outputId": "8188949a-7f9b-4ca0-d151-deea2b45802e"
},
"outputs": [
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" title=\"Suggest charts.\"\n",
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"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
" </div>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const containerElement = document.querySelector('#' + key);\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" }\n",
" </script>\n",
"\n",
" <script>\n",
"\n",
"function displayQuickchartButton(domScope) {\n",
" let quickchartButtonEl =\n",
" domScope.querySelector('#df-308e4096-91e5-46d2-8ce8-e56be58b8a08 button.colab-df-quickchart');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"}\n",
"\n",
" displayQuickchartButton(document);\n",
" </script>\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-0b9f6632-c877-40cf-9e2f-553dbf81fe43 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-0b9f6632-c877-40cf-9e2f-553dbf81fe43');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n"
],
"text/plain": [
" id values sparse_values \\\n",
"0 Ben_0 [0.0, 0.0, 0.0, 125628656.0, 0.0, 0.0, 5421442... None \n",
"1 Ben_1 [0.0, 0.0, 0.0, 356751744.0, 1190461440.0, 0.0... None \n",
"2 Ben_2 [0.0, 0.0, 0.0, 356751680.0, 1190461440.0, 0.0... None \n",
"3 Ben_3 [0.0, 0.0, 0.0, 125515856.0, 0.0, 0.0, 5432884... None \n",
"4 Ben_4 [0.0, 0.0, 0.0, 26214912.0, 698683840.0, 0.0, ... None \n",
"\n",
" metadata blob \n",
"0 {'label': 'Benign'} None \n",
"1 {'label': 'Benign'} None \n",
"2 {'label': 'Benign'} None \n",
"3 {'label': 'Benign'} None \n",
"4 {'label': 'Benign'} None "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pinecone_datasets import load_dataset\n",
"\n",
"dataset = load_dataset(\"it-threat-data-train\")\n",
"dataset.head()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "sCkVwqm2KyM4"
},
"source": [
"We don't need the `sparse_values` and `blob` columns so we drop:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "-psXytDLRAeo",
"outputId": "4eddafd5-78c0-4b12-ff14-e2f012c5a3f1"
},
"outputs": [
{
"data": {
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" <th>values</th>\n",
" <th>metadata</th>\n",
" </tr>\n",
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" <th>0</th>\n",
" <td>Ben_0</td>\n",
" <td>[0.0, 0.0, 0.0, 125628656.0, 0.0, 0.0, 5421442...</td>\n",
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" <td>{'label': 'Benign'}</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>Ben_2</td>\n",
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" <td>{'label': 'Benign'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Ben_3</td>\n",
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" <td>{'label': 'Benign'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Ben_4</td>\n",
" <td>[0.0, 0.0, 0.0, 26214912.0, 698683840.0, 0.0, ...</td>\n",
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" width=\"24px\">\n",
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" displayQuickchartButton(document);\n",
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" display:flex;\n",
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"\n",
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"\n",
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" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
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" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
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" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
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" const buttonEl =\n",
" document.querySelector('#df-9a89e026-f2e7-4e6b-8d2c-46bef4ae0df9 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-9a89e026-f2e7-4e6b-8d2c-46bef4ae0df9');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n"
],
"text/plain": [
" id values \\\n",
"0 Ben_0 [0.0, 0.0, 0.0, 125628656.0, 0.0, 0.0, 5421442... \n",
"1 Ben_1 [0.0, 0.0, 0.0, 356751744.0, 1190461440.0, 0.0... \n",
"2 Ben_2 [0.0, 0.0, 0.0, 356751680.0, 1190461440.0, 0.0... \n",
"3 Ben_3 [0.0, 0.0, 0.0, 125515856.0, 0.0, 0.0, 5432884... \n",
"4 Ben_4 [0.0, 0.0, 0.0, 26214912.0, 698683840.0, 0.0, ... \n",
"\n",
" metadata \n",
"0 {'label': 'Benign'} \n",
"1 {'label': 'Benign'} \n",
"2 {'label': 'Benign'} \n",
"3 {'label': 'Benign'} \n",
"4 {'label': 'Benign'} "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.documents.drop(['sparse_values', 'blob'], axis=1, inplace=True)\n",
"dataset.head()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "V3dx9XkPYTNV"
},
"source": [
"### Upload Data"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "6C7er-8Gl2Rg"
},
"source": [
"You can lower the `num_items` and, by doing so, limit the number of uploaded items."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "8iIbuW52zZVy",
"outputId": "93d97fc3-b828-4809-a24e-f36992f5cd3d"
},
"outputs": [
{
"data": {
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"\n",
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" </svg>\n",
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"\n",
"\n",
"\n",
" <div id=\"df-886db082-9a9f-451f-8330-4ff06e161b53\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-886db082-9a9f-451f-8330-4ff06e161b53')\"\n",
" title=\"Suggest charts.\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
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" </g>\n",
"</svg>\n",
" </button>\n",
" </div>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const containerElement = document.querySelector('#' + key);\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" }\n",
" </script>\n",
"\n",
" <script>\n",
"\n",
"function displayQuickchartButton(domScope) {\n",
" let quickchartButtonEl =\n",
" domScope.querySelector('#df-886db082-9a9f-451f-8330-4ff06e161b53 button.colab-df-quickchart');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"}\n",
"\n",
" displayQuickchartButton(document);\n",
" </script>\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
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"\n",
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" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
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"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-3dbeb309-5fed-403b-a154-3e7ac85f1993 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-3dbeb309-5fed-403b-a154-3e7ac85f1993');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n"
],
"text/plain": [
" id values \\\n",
"0 Ben_0 [0.0, 0.0, 0.0, 125628656.0, 0.0, 0.0, 5421442... \n",
"1 Ben_1 [0.0, 0.0, 0.0, 356751744.0, 1190461440.0, 0.0... \n",
"2 Ben_2 [0.0, 0.0, 0.0, 356751680.0, 1190461440.0, 0.0... \n",
"3 Ben_3 [0.0, 0.0, 0.0, 125515856.0, 0.0, 0.0, 5432884... \n",
"4 Ben_4 [0.0, 0.0, 0.0, 26214912.0, 698683840.0, 0.0, ... \n",
"\n",
" metadata \n",
"0 {'label': 'Benign'} \n",
"1 {'label': 'Benign'} \n",
"2 {'label': 'Benign'} \n",
"3 {'label': 'Benign'} \n",
"4 {'label': 'Benign'} "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 99,
"referenced_widgets": [
"d457fe5de4784553a9f47d7b4462ea4c",
"cd5ed6888cab4656891522f1765eaf04",
"ac390e5f1b9547318ab0478a86d0cf6b",
"d0aeac530caa41a1b4c0ed56facfaefb",
"da08a329e02147ca9f7029143682703f",
"1c9e4ed606d1463e80f241e598b08fb8",
"7cad2cfda84d4c1aa8898b8dff361a8b",
"d3eb91a8850843b5833857901c1b76a3",
"05b173e450494565b8d28c6585a527af",
"f2d5170a93244298ba24b3f14b11f0c7",
"da1de055c6834a82a69da37d5fa889e7",
"80e09a9ff31d454fb6614f4923ff6433",
"a6681a2392e04c91a77b8f8939c5bd7d",
"e10a0235c9a949c8869b46b48c67b58c",
"f52116b4976c4b1e8b408f09440ed52f",
"0f90c5e13f034f85990c48026058ec32",
"8e150825351a44dd94b792cefc96365c",
"3ca5817c63cb4127b31f4973b7670388",
"c53b3d1aaf524b62b293368c23ff358f",
"9908e231310244d69be88a265e565be7",
"a42a08cf00b140ebb4c883bde04febfb",
"c0dec6e0d5ea4d09ae473aa9e498be1d"
]
},
"id": "_Ti9p0P-ywpX",
"outputId": "728fc729-aa25-44ba-afd6-bb7e3a05b8aa",
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d457fe5de4784553a9f47d7b4462ea4c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"sending upsert requests: 0%| | 0/50000 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "80e09a9ff31d454fb6614f4923ff6433",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"collecting async responses: 0%| | 0/100 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"upserted_count: 50000"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"num_items = 50_000\n",
"dataset = dataset.documents[:num_items]\n",
"\n",
"index.upsert_from_dataframe(dataset, batch_size=500)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "fglWJfAq_kw3"
},
"source": [
"Let's verify all items were inserted."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xU172A4EywpY",
"outputId": "14ef7372-44f8-4fcf-ea1e-7d41e5f181ad"
},
"outputs": [
{
"data": {
"text/plain": [
"{'dimension': 128,\n",
" 'index_fullness': 0.5,\n",
" 'namespaces': {'': {'vector_count': 50000}},\n",
" 'total_vector_count': 50000}"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"index.describe_index_stats()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "3CGzW3mVywpY"
},
"source": [
"## Query"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "Ywuld4BylAIu"
},
"source": [
"First, we will randomly select a Benign/Attack event and query the vector index using the event embedding. Then, we will use data from different day, that contains same set of attacks to query on a bigger sample."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "tLs_wKDQRAeq"
},
"source": [
"### Load the Model"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "NDLNS7zYRAeq"
},
"source": [
"Here we load the pretrained model. The model is trained using the data from the same date."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "PzWle9Q9RAeq"
},
"source": [
"We have modified [the original model](https://github.com/rambasnet/DeepLearning-IDS/blob/master/keras_tensorflow_models/02-23-2018.csv_adam_10_10_multiclass_baseline_model_1561316601.model) slightly and changed the number of classes from four (Benign, BruteForce-Web, BruteForce-XSS, SQL-Injection) to two (Benign and Attack). In the step below we will download and unzip our modified model."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"id": "jR6hucSjRAeq"
},
"outputs": [],
"source": [
"!wget -q -O it_threat_model.model.zip \"https://drive.google.com/uc?export=download&id=1VYMHOk_XMAc-QFJ_8CAPvWFfHnLpS2J_\"\n",
"!unzip -q it_threat_model.model.zip"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"id": "9zRyWjP8RAer"
},
"outputs": [],
"source": [
"from tensorflow import keras\n",
"from tensorflow.keras.models import Model\n",
"import tensorflow.keras.backend as K"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WPNez6x_RAer",
"outputId": "28f36a01-d0ba-4abc-a88a-1c1268e2a10a"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named \"keras_metadata.pb\" in the SavedModel directory.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" dense (Dense) (None, 128) 10240 \n",
" \n",
" dense_1 (Dense) (None, 64) 8256 \n",
" \n",
" dense_2 (Dense) (None, 1) 65 \n",
" \n",
"=================================================================\n",
"Total params: 18,561\n",
"Trainable params: 18,561\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model = keras.models.load_model('it_threat_model.model')\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"id": "qRouEveTRAer"
},
"outputs": [],
"source": [
"# Select the first layer\n",
"layer_name = 'dense'\n",
"intermediate_layer_model = Model(inputs=model.input,\n",
" outputs=model.get_layer(layer_name).output)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "m5H4rMyOYTNX"
},
"source": [
"\n",
"### Evaluate the Rare Event Classification Model"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "velMK_XlYTNX"
},
"source": [
"We will use network intrusion precomputed dataset for 22-02-2018 for querying and testing the Pinecone."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "ajLLdSjRRAes",
"outputId": "de277b3a-d1c9-450d-c61a-92bb0187794e"
},
"outputs": [
{
"data": {
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" <td>None</td>\n",
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" <td>Ben_3</td>\n",
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" <td>None</td>\n",
" <td>{'label': 'Benign'}</td>\n",
" <td>None</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>Ben_4</td>\n",
" <td>[0.0, 0.0, 0.0, 26222988.0, 698676736.0, 0.0, ...</td>\n",
" <td>None</td>\n",
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" width: 32px;\n",
" }\n",
"\n",
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" }\n",
"\n",
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" fill: #D2E3FC;\n",
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" background-color: #434B5C;\n",
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" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-db92e0c8-69c7-4a37-aee2-51394b816bb5 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-db92e0c8-69c7-4a37-aee2-51394b816bb5');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n"
],
"text/plain": [
" id values sparse_values \\\n",
"0 Ben_0 [0.0, 0.0, 0.0, 170976752.0, 187935920.0, 0.0,... None \n",
"1 Ben_1 [0.0, 0.0, 0.0, 211572592.0, 0.0, 0.0, 7663978... None \n",
"2 Ben_2 [0.0, 0.0, 0.0, 133747560.0, 770489344.0, 0.0,... None \n",
"3 Ben_3 [0.0, 0.0, 0.0, 133747288.0, 770489344.0, 0.0,... None \n",
"4 Ben_4 [0.0, 0.0, 0.0, 26222988.0, 698676736.0, 0.0, ... None \n",
"\n",
" metadata blob \n",
"0 {'label': 'Benign'} None \n",
"1 {'label': 'Benign'} None \n",
"2 {'label': 'Benign'} None \n",
"3 {'label': 'Benign'} None \n",
"4 {'label': 'Benign'} None "
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset_22 = load_dataset(\"it-threat-data-test\")\n",
"dataset_22.head()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "in1nkunfRAes",
"outputId": "33e76884-bd3f-4224-adf3-cd13a4dea35f"
},
"outputs": [
{
"data": {
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" <th>metadata</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
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" <th>0</th>\n",
" <td>Ben_0</td>\n",
" <td>[0.0, 0.0, 0.0, 170976752.0, 187935920.0, 0.0,...</td>\n",
" <td>{'label': 'Benign'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Ben_1</td>\n",
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" <th>3</th>\n",
" <td>Ben_3</td>\n",
" <td>[0.0, 0.0, 0.0, 133747288.0, 770489344.0, 0.0,...</td>\n",
" <td>{'label': 'Benign'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Ben_4</td>\n",
" <td>[0.0, 0.0, 0.0, 26222988.0, 698676736.0, 0.0, ...</td>\n",
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" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
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" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
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],
"text/plain": [
" id values \\\n",
"0 Ben_0 [0.0, 0.0, 0.0, 170976752.0, 187935920.0, 0.0,... \n",
"1 Ben_1 [0.0, 0.0, 0.0, 211572592.0, 0.0, 0.0, 7663978... \n",
"2 Ben_2 [0.0, 0.0, 0.0, 133747560.0, 770489344.0, 0.0,... \n",
"3 Ben_3 [0.0, 0.0, 0.0, 133747288.0, 770489344.0, 0.0,... \n",
"4 Ben_4 [0.0, 0.0, 0.0, 26222988.0, 698676736.0, 0.0, ... \n",
"\n",
" metadata \n",
"0 {'label': 'Benign'} \n",
"1 {'label': 'Benign'} \n",
"2 {'label': 'Benign'} \n",
"3 {'label': 'Benign'} \n",
"4 {'label': 'Benign'} "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset_22.documents.drop(['sparse_values', 'blob'], axis=1, inplace=True)\n",
"dataset_22.head()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "P6nJdtveYTNY"
},
"source": [
"Let's define a sample that will include all different types of web attacks for this specific date."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "N7vwZk6HYTNY",
"outputId": "7ad7ec0d-f18b-4fba-c5b5-a2477a786a7a"
},
"outputs": [
{
"data": {
"text/plain": [
"metadata\n",
"{'label': 'Benign'} 1638\n",
"{'label': 'Brute Force -Web'} 249\n",
"{'label': 'Brute Force -XSS'} 79\n",
"{'label': 'SQL Injection'} 34\n",
"Name: count, dtype: int64"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_sample = dataset_22.documents[-2000:]\n",
"data_sample.metadata.value_counts()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "neSxNwYckGMK"
},
"source": [
"Now, we will query the test dataset and save predicted and expected results to create a confusion matrix."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"a654b98888a643f8b72d652c85a8fb6a",
"62917e73c7eb48268506e5de72e6a233",
"336b75c3eb5343dab90ac197784fe0a4",
"5549d01ac4ac4041a0aa4ccaa7f3ee81",
"1e482c1ab9ec47de9eb84f4abe544fcf",
"59e9e2fd6b0b474d9d7f006a68b2e9ff",
"cccfc5f59db740d689899d2fc783e4f4",
"3a8bbd8bbc7044d2a54a4c2e877e2532",
"c234a54fcaf14d0ba850eea647df6c56",
"b3f2befb0abb42869f2e8066d2ae137c",
"a95a29c73700484d9bb6dfcdc293490c"
]
},
"id": "8u6cg_1tYTNY",
"outputId": "f8536aa0-e2e3-49ef-c8b7-09f3db977b6a",
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a654b98888a643f8b72d652c85a8fb6a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/20 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from tqdm.auto import tqdm\n",
"from collections import Counter\n",
"\n",
"y_true = []\n",
"y_pred = []\n",
"\n",
"BATCH_SIZE = 100\n",
"\n",
"for i in tqdm(range(0, len(data_sample), BATCH_SIZE)):\n",
" test_data = data_sample.iloc[i:i+BATCH_SIZE, :]\n",
" test_vector = test_data['values'].tolist()\n",
"\n",
" # Query using the vector embedding\n",
" query_results = []\n",
"\n",
" for xq in test_vector:\n",
" query_res = index.query(xq.tolist(), top_k=50)\n",
" query_results.append(query_res)\n",
"\n",
" ids = [res.id for result in query_results for res in result.matches]\n",
"\n",
" for label, res in zip(test_data.metadata.values, query_results):\n",
" # Add to the true list\n",
" if label['label'] == 'Benign':\n",
" y_true.append(0)\n",
" else:\n",
" y_true.append(1)\n",
"\n",
" counter = Counter(match.id.split('_')[0] for match in res.matches)\n",
"\n",
" # Add to the predicted list\n",
" if counter['Bru'] or counter['SQL']:\n",
" y_pred.append(1)\n",
" else:\n",
" y_pred.append(0)\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"id": "t5lG_ePFRAe9"
},
"outputs": [],
"source": [
"!pip install seaborn -qU"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"id": "dj2tAS6YRAe9"
},
"outputs": [],
"source": [
"from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
"from sklearn.metrics import confusion_matrix\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 490
},
"id": "HV3-gkdWYTNZ",
"outputId": "e7628098-81cc-4885-9d5c-4a74c15d360b"
},
"outputs": [
{
"data": {
"text/plain": [
"[Text(0, 0.5, 'Benign'), Text(0, 1.5, 'Attack')]"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create confusion matrix\n",
"conf_matrix = confusion_matrix(y_true, y_pred)\n",
"\n",
"# Show confusion matrix\n",
"ax = plt.subplot()\n",
"sns.heatmap(conf_matrix, annot=True, ax = ax, cmap='Blues', fmt='g', cbar=False)\n",
"\n",
"# Add labels, title and ticks\n",
"ax.set_xlabel('Predicted')\n",
"ax.set_ylabel('Actual')\n",
"ax.set_title('Confusion Matrix')\n",
"ax.xaxis.set_ticklabels(['Benign', 'Attack'])\n",
"ax.yaxis.set_ticklabels(['Benign', 'Attack'])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "-H7rWFguYTNZ"
},
"source": [
"Now we can calculate overall accuracy and per class accuracy."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uHTInLt2YTNZ",
"outputId": "238ccc0c-2bbc-4d3c-98ef-2f7101cb1325"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.819\n",
"Precision: 0.000\n",
"Recall: 0.000\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
}
],
"source": [
"# Calculate accuracy\n",
"acc = accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)\n",
"precision = precision_score(y_true, y_pred)\n",
"recall = recall_score(y_true, y_pred)\n",
"\n",
"print(f\"Accuracy: {acc:.3f}\")\n",
"print(f\"Precision: {precision:.3f}\")\n",
"print(f\"Recall: {recall:.3f}\")"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 112
},
"id": "ZNzyqAH9YTNZ",
"outputId": "56e19840-fb6b-4296-96c9-3417c14ebe4b"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
" <div id=\"df-44b9a897-7181-45c9-99be-f65ff3041806\">\n",
" <div class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>type</th>\n",
" <th>accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Benign</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Attack</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44b9a897-7181-45c9-99be-f65ff3041806')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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"text/plain": [
" type accuracy\n",
"0 Benign 1.0\n",
"1 Attack 0.0"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Calculate per class accuracy\n",
"cmd = confusion_matrix(y_true, y_pred, normalize=\"true\").diagonal()\n",
"per_class_accuracy_df = pd.DataFrame([(index, round(value,4)) for index, value in zip(['Benign', 'Attack'], cmd)], columns = ['type', 'accuracy'])\n",
"per_class_accuracy_df = per_class_accuracy_df.round(2)\n",
"display(per_class_accuracy_df)"
]
},
{
"attachments": {},
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"metadata": {
"id": "Gfy_LW5zXIj6"
},
"source": [
"We got great results using Pinecone! In [our longer example](https://colab.research.google.com/github/pinecone-io/examples/blob/master/learn/security/it-threat-detection.ipynb) we also see what happens if we skip the similarity search step and predict values from the model directly. In short, it performs much worse! By adding Pinecone's similarity search over the same model's embeddings we improve threat detection (i.e., \"Attack\") accuracy by over 50%!"
]
},
{
"attachments": {},
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"metadata": {
"id": "OiT0JgQxktOC"
},
"source": [
"### Result summary"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "UBBv6-dnfEeX"
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"source": [
"Using standard vector embeddings with Pinecone's similarity search service, we detected 85% of the attacks while keeping a low 3% false-positive rate. In [our longer example](https://colab.research.google.com/github/pinecone-io/examples/blob/master/learn/security/it-threat-detection.ipynb), we also showed that our similarity search approach outperforms the direct classification approach that utilizes the classifier's embedding model. Similarity search-based detection gained 50% higher accuracy compared to the direct detector.\n",
"\n",
"[Original published results](https://github.com/rambasnet/DeepLearning-IDS/blob/master/graphics/confusion_matrices/) for 02-22-2018 show that the model was able to correctly detect 208520 benign cases out of 208520 benign cases, and 24 (18+1+5) attacks out of 70 attacks in the test set making this model **34.3% accurate in predicting attacks**. For testing purposes, 20% of the data for 02-22-2018 was used.\n",
"\n",
"![02-22-2018--6-15%281%29.png](https://raw.githubusercontent.com/rambasnet/DeepLearning-IDS/master/graphics/confusion_matrices/02-22-2018--6-15(1).png)\n",
"\n",
"As you can see, the model's performance for creating embeddings for Pinecone was much higher.\n",
"\n",
"The model we have created follows the academic paper ([model for the same date](https://github.com/rambasnet/DeepLearning-IDS/blob/master/keras_tensorflow_models/) (02-23-2018)) and is slightly modified, but still a straightforward, sequential, shallow model. We have changed the number of classes from four (Benign, BruteForce-Web, BruteForce-XSS, SQL-Injection) to two (Benign and Attack), only interested in whether we are detecting an attack or not. We have also changed validation metrics to precision and recall. These changes improved our results. Yet, there is still room for further improvements, for example, by adding more data covering multiple days and different types of attacks."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "JdZcEl1pfEeX"
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"source": [
"## Delete the Index"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "hyxLeOnSfEeX"
},
"source": [
"Delete the index once you are sure that you do not want to use it anymore. Once it is deleted, you cannot reuse it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ywvaJrVkfEeX"
},
"outputs": [],
"source": [
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "Hu2mIbHms3k7"
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"source": [
"---"
]
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