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Prototype Beta, Graph Convolutional Network Embeddings with BERT with Sysmon data
This commit is contained in:
Marius Ciepluch
parent
ac1311744c
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da68bc33ba
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Prototype_Beta_GCN_BERT_Embedding_Sysmon.ipynb
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Prototype_Beta_GCN_BERT_Embedding_Sysmon.ipynb
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"id": "53fefa0e-f261-47a5-9481-9c00e0d68e25",
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"metadata": {
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"Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
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"Requirement already satisfied: torch==2.2.1+cpu in /home/marius/anaconda3/lib/python3.11/site-packages (2.2.1+cpu)\n",
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"source": [
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"!pip install torch==2.2.1+cpu -f https://download.pytorch.org/whl/torch_stable.html\n",
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"!pip install torch-geometric -f https://data.pyg.org/whl/torch-2.2.1+cpu.html\n",
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"!pip install torch-scatter -f https://data.pyg.org/whl/torch-2.2.1+cpu.html\n",
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"id": "f6c8b735-a54b-4485-bd16-34fad500f60f",
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"/home/marius/anaconda3/lib/python3.11/site-packages/transformers/utils/generic.py:260: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
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" torch.utils._pytree._register_pytree_node(\n"
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import torch\n",
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"import torch.nn.functional as F\n",
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"from transformers import BertTokenizer, BertModel\n",
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"import networkx as nx\n",
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"from torch_geometric.data import Data\n",
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"from torch_geometric.nn import GCNConv\n",
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"from torch_geometric.utils import from_networkx"
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"execution_count": 8,
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"id": "ee9877b3-ece3-4f37-bc1b-73e0994325c7",
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"metadata": {
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"tags": []
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"text": [
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"GCN embeddings shape: torch.Size([7, 768])\n",
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"tensor([[-0.2238, -0.1572, 0.0377, ..., -0.1945, -0.2714, 0.7107],\n",
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" [-0.0212, -0.0117, 0.0010, ..., -0.0172, -0.0238, 0.0644],\n",
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" [-0.0212, -0.0117, 0.0010, ..., -0.0172, -0.0238, 0.0644],\n",
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" ...,\n",
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" [-0.0212, -0.0117, 0.0010, ..., -0.0172, -0.0238, 0.0644],\n",
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" [-0.0212, -0.0117, 0.0010, ..., -0.0172, -0.0238, 0.0644],\n",
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" [-0.0212, -0.0117, 0.0010, ..., -0.0172, -0.0238, 0.0644]])\n"
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]
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],
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"source": [
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"import pandas as pd\n",
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"import torch\n",
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"import torch.nn.functional as F\n",
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"import networkx as nx\n",
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"from transformers import BertTokenizer, BertModel\n",
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"from torch_geometric.data import Data\n",
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"from torch_geometric.nn import GCNConv\n",
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"from torch_geometric.utils import from_networkx\n",
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"\n",
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"# Sample CSV data\n",
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"data = {\n",
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" \"Timestamp\": [\"2024-05-15T16:00:15.887Z\"],\n",
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" \"OS\": [\"win10\"],\n",
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" \"IP\": [\"fe80::24b4:3691:44a6:38a1\"],\n",
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" \"LogLevel\": [\"information\"],\n",
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" \"EventID\": [5379],\n",
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" \"Category\": [\"User Account Management\"],\n",
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" \"Description\": [\n",
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" \"\"\"Credential Manager credentials were read.\n",
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"\n",
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" Subject:\n",
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" Security ID: S-1-5-18\n",
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" Account Name: WIN10$\n",
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" Account Domain: sec699-20\n",
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" Logon ID: 0x3E7\n",
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" Read Operation: Enumerate Credentials\n",
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"\n",
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" This event occurs when a user performs a read operation on stored credentials in Credential Manager.\"\"\"\n",
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" ]\n",
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"}\n",
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"\n",
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"# Convert to DataFrame\n",
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"df = pd.DataFrame(data)\n",
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"\n",
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"# Function to parse hierarchical data from description\n",
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"def parse_description(description):\n",
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" hierarchy = {}\n",
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" lines = description.split('\\n')\n",
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" key = None\n",
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" for line in lines:\n",
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" line = line.strip()\n",
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" if not line:\n",
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" continue\n",
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" if ':' in line:\n",
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" parts = line.split(':', 1)\n",
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" key = parts[0].strip()\n",
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" value = parts[1].strip()\n",
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" hierarchy[key] = value\n",
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" elif key:\n",
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" hierarchy[key] += ' ' + line.strip()\n",
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" return hierarchy\n",
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"\n",
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"# Apply parsing to the Description column\n",
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"df['ParsedDescription'] = df['Description'].apply(parse_description)\n",
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"\n",
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"# Initialize graph\n",
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"G = nx.Graph()\n",
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"\n",
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"# Load pre-trained BERT model and tokenizer\n",
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"tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
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"model = BertModel.from_pretrained('bert-base-uncased')\n",
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"\n",
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"# Add nodes and edges to the graph\n",
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"for index, row in df.iterrows():\n",
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" parsed = row['ParsedDescription']\n",
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" description = row['Description']\n",
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" \n",
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" # Tokenize and encode the description\n",
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" inputs = tokenizer(description, return_tensors='pt', max_length=512, truncation=True, padding='max_length')\n",
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" outputs = model(**inputs)\n",
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" description_embedding = outputs.last_hidden_state[:, 0, :].detach().numpy().flatten()\n",
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" \n",
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" # Add event node with BERT embedding\n",
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" event_node = 'Event_{}'.format(index)\n",
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|
" G.add_node(event_node, type='event', embedding=description_embedding)\n",
|
||||||
|
" \n",
|
||||||
|
" # Add other nodes and connect to event node\n",
|
||||||
|
" for key, value in parsed.items():\n",
|
||||||
|
" if not G.has_node(value):\n",
|
||||||
|
" G.add_node(value, type=key, embedding=torch.zeros(768).numpy()) # Add default embedding for non-event nodes\n",
|
||||||
|
" G.add_edge(event_node, value)\n",
|
||||||
|
"\n",
|
||||||
|
"# Convert NetworkX graph to PyTorch Geometric data\n",
|
||||||
|
"data = from_networkx(G)\n",
|
||||||
|
"\n",
|
||||||
|
"# Add node features to the data object\n",
|
||||||
|
"node_embeddings = [G.nodes[node]['embedding'] for node in G.nodes]\n",
|
||||||
|
"data.x = torch.tensor(node_embeddings, dtype=torch.float)\n",
|
||||||
|
"\n",
|
||||||
|
"# Define a simple GCN model\n",
|
||||||
|
"class GCN(torch.nn.Module):\n",
|
||||||
|
" def __init__(self):\n",
|
||||||
|
" super(GCN, self).__init__()\n",
|
||||||
|
" self.conv1 = GCNConv(data.num_node_features, 256)\n",
|
||||||
|
" self.conv2 = GCNConv(256, 128)\n",
|
||||||
|
" self.conv3 = GCNConv(128, 768) # Output size matches the input embedding size\n",
|
||||||
|
"\n",
|
||||||
|
" def forward(self, data):\n",
|
||||||
|
" x, edge_index = data.x, data.edge_index\n",
|
||||||
|
" x = self.conv1(x, edge_index)\n",
|
||||||
|
" x = F.relu(x)\n",
|
||||||
|
" x = self.conv2(x, edge_index)\n",
|
||||||
|
" x = F.relu(x)\n",
|
||||||
|
" x = self.conv3(x, edge_index)\n",
|
||||||
|
" return x\n",
|
||||||
|
"\n",
|
||||||
|
"# Create and train the model\n",
|
||||||
|
"model = GCN()\n",
|
||||||
|
"optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)\n",
|
||||||
|
"\n",
|
||||||
|
"model.train()\n",
|
||||||
|
"for epoch in range(200):\n",
|
||||||
|
" optimizer.zero_grad()\n",
|
||||||
|
" out = model(data)\n",
|
||||||
|
" loss = F.mse_loss(out, data.x) # Dummy loss for example purposes\n",
|
||||||
|
" loss.backward()\n",
|
||||||
|
" optimizer.step()\n",
|
||||||
|
"\n",
|
||||||
|
"# Extract the embeddings from the trained model\n",
|
||||||
|
"model.eval()\n",
|
||||||
|
"with torch.no_grad():\n",
|
||||||
|
" embeddings = model(data)\n",
|
||||||
|
"\n",
|
||||||
|
"# Print the embeddings\n",
|
||||||
|
"print(\"GCN embeddings shape:\", embeddings.shape)\n",
|
||||||
|
"print(embeddings)\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "bdb7c728-a8c2-4e89-bea8-d0f31585a7cc",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": []
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3 (ipykernel)",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.11.5"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
Loading…
Reference in New Issue
Block a user