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base: alu:61a451b82d9ddd856c9d3f865c03a09ecede86b7
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alu:main alu:jai_runs alu:decision-tree-nudge-boundaries alu:Parser
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compare: alu:main
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alu:main alu:jai_runs alu:decision-tree-nudge-boundaries alu:Parser

15 Commits
61a451b82d ... main

Author SHA1 Message Date
Arthur Lu
51f920e2ba upload example tree based on 10-01 data 2025-06-20 03:18:09 +00:00
Arthur Lu
1136bd93ea update readme 2025-06-14 03:10:48 +00:00
Arthur Lu
2ad40946d1 update treetormt 2025-06-12 19:53:46 +00:00
Arthur Lu
50075b1acc Merge remote-tracking branch 'origin/decision-tree-nudge-boundaries' 2025-06-12 19:52:02 +00:00
Jai Parera
1585399c7d Fixed loop ordering and path_to_class in JSON 2025-06-11 23:37:33 -07:00
Arthur Lu
8301998da3 temp fix for issue with metadata 2025-06-12 06:10:39 +00:00
Arthur Lu
3b2d6b3186 fix eval bug in parser 2025-06-12 05:37:48 +00:00
Jai Parera
24fc2ed6f7 Add support for combined datasets and analysis 2025-06-11 20:38:37 -07:00
Nathan Huey
fda251f051 First try boundary nudging 2025-06-11 16:55:23 -07:00
Arthur Lu
541538fcfe update decision tree results 2025-06-11 22:37:38 +00:00
Arthur Lu
afc882a569 Merge pull request #5 from ltcptgeneral/Parser 
Fix range bug in TreeCompress, update parser to autofail non-singular…
 2025-06-11 12:11:39 -07:00
Arthur Lu
6de3807fe2 fix range bug in TreeCompress, update parser to autofail non-singular classifications 2025-06-11 19:10:19 +00:00
Arthur Lu
fc16d3c586 Merge pull request #4 from ltcptgeneral/Parser 
Eval compressed tree accuracy
 2025-06-11 11:28:38 -07:00
Arthur Lu
7bee40ecf9 restore TreeCompress and TreeToRMT from main 2025-06-11 18:28:18 +00:00
krishpatel
e811171a73 Implemented working compressed tree parser to get classification accuracy 2025-06-11 11:10:49 -07:00
21 changed files with 4201 additions and 43 deletions
Expand all files Collapse all files

2
.gitattributes vendored Normal file
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@@ -0,0 +1,2 @@
# force LF for any shell script
*.sh text eol=lf

4
.gitignore vendored
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@@ -1,3 +1,5 @@
data.*
__pycache__
*.json
data/*
.DS_Store
.ipynb_checkpoints/

152
CompressedTreeParser.ipynb Normal file
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@@ -0,0 +1,152 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 138,
"id": "938dec51",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import argparse\n",
"from sklearn.tree import DecisionTreeClassifier, plot_tree, _tree\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn.tree import export_graphviz\n",
"import pydotplus\n",
"from matplotlib import pyplot as plt\n",
"from labels import mac_to_label\n",
"import json\n",
"import math"
]
},
{
"cell_type": "code",
"execution_count": 139,
"id": "442624c7",
"metadata": {},
"outputs": [],
"source": [
"Set1 = pd.read_csv('data/combined/data.csv').values.tolist()\n",
"X = [i[0:3] for i in Set1]\n",
"Y =[i[3] for i in Set1]"
]
},
{
"cell_type": "code",
"execution_count": 142,
"id": "12ad454d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'0': 20, '1': 20, '2': 9, '3': 20, '4': 0, '5': 13, '6': 20, '7': 0, '8': 12, '9': 4, '10': 20, '11': 4, '12': 1, '13': 16, '14': 20, '15': 2, '16': 20, '17': 0, '18': 20, '19': 20, '20': 20, '21': 20, '22': 20, '23': 1, '24': 2, '25': 20, '26': 13, '27': 11, '28': 20, '29': 20}\n"
]
}
],
"source": [
"predict_Yt = []\n",
"index=0\n",
"\n",
"with open('compressed_tree.json', 'r') as file:\n",
" data = json.load(file)\n",
" classes = data[\"classes\"]\n",
" for x in X:\n",
" counter = 0\n",
" class_set = []\n",
" paths_set = []\n",
" features = [\"protocol\", \"src\", \"dst\"]\n",
" for feature in features:\n",
" if feature in data[\"layers\"]:\n",
" for node in data['layers'][feature]:\n",
" if node['min'] is None:\n",
" if x[counter] <= node['max']:\n",
" class_set.append(node['classes'])\n",
" paths_set.append(node[\"paths\"])\n",
" break #is this an issue?\n",
" else:\n",
" continue\n",
" elif node['max'] is None:\n",
" if node['min'] < x[counter]:\n",
" class_set.append(node['classes'])\n",
" paths_set.append(node[\"paths\"])\n",
" break #is this an issue?\n",
" else:\n",
" continue\n",
" elif node['min'] < x[counter] and x[counter] <= node['max']:\n",
" class_set.append(node['classes'])\n",
" paths_set.append(node[\"paths\"])\n",
" break #is this an issue?\n",
"\n",
" counter += 1\n",
" result = set(class_set[0])\n",
" paths = set(paths_set[0])\n",
" for s in class_set[1:]:\n",
" result.intersection_update(s)\n",
" for s in paths_set[1:]:\n",
" paths.intersection_update(s)\n",
"\n",
" #predict_Yt.append(list(result))\n",
" #print(result)\n",
" if len(paths) != 1:\n",
" print(paths)\n",
" print(x)\n",
" print(result)\n",
" assert len(paths) == 1\n",
" path = list(paths)[0]\n",
" pred = data[\"path_to_class\"][str(path)]\n",
" pred_class = classes[pred]\n",
" predict_Yt.append(pred_class)\n",
" \n",
" index += 1"
]
},
{
"cell_type": "code",
"execution_count": 143,
"id": "8b4c56b6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8410252791654538\n"
]
}
],
"source": [
"correct = 0\n",
"for i in range(len(Y)):\n",
" prediction = predict_Yt[i]\n",
" if prediction != None and Y[i] == prediction:\n",
" correct += 1\n",
"\n",
"print(correct / len(Y))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

150
DecisionTree.ipynb
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File diff suppressed because one or more lines are too long

4
ExtractDataset.ipynb
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@@ -89,7 +89,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "switch",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -103,7 +103,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.7"
"version": "3.12.9"
}
},
"nbformat": 4,

20
README.md
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@@ -2,18 +2,30 @@
Run `pip install -r requirements.txt`
Run `setup.sh`
# Tree Generation
## Download Dataset
Download the *September 22 2016* dataset from: https://iotanalytics.unsw.edu.au/iottraces.html#bib18tmc
Download the *September 22 2016* dataset (or others) from: https://iotanalytics.unsw.edu.au/iottraces.html#bib18tmc
Rename the file as data.pcap
Place these into the `data/tar` folder.
Run `extract_tars.sh` which will extract and place the `.pcap` files at the corresponding location inside `data/pcap`.
## Preprocessing Dataset
Run `ExtractDataset.ipynb`, this will take a few minutes
Run `extract_all_datasets.py` which will extract the data from each file in `data/pcap` and turn it into the corresponding `.csv` file inside `data/processed`. This will take a few minutes per file. Combine the data under `data/csv` using `combine_csv.py`. This will overwrite `data/combined/data.csv` which you can use for the decision tree.
## Training
Run `DecisionTree.ipynb`, the tree should be output in `tree`
Run `DecisionTree.ipynb`, the tree should be output in `tree.json`
## Compression
Run `TreeCompress.ipynb`, the tree should be output in `compressed_tree.json`
## RMT
Run `TreeToRMT.ipynb`, it will report the TCAM and SRAM usage of the compressed tree

34
TreeCompress.ipynb
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@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 73,
"id": "ec310f34",
"metadata": {},
"outputs": [],
@@ -14,7 +14,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 74,
"id": "5b54797e",
"metadata": {},
"outputs": [],
@@ -28,7 +28,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 75,
"id": "a38fdb8a",
"metadata": {},
"outputs": [],
@@ -60,7 +60,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 76,
"id": "2fd4f738",
"metadata": {},
"outputs": [],
@@ -83,7 +83,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 77,
"id": "98cde024",
"metadata": {},
"outputs": [],
@@ -109,13 +109,13 @@
"\t\tvalue = condition[\"value\"]\n",
"\n",
"\t\t# move the min/max for the corresponding feature in compressed\n",
"\t\tif operation == \"<=\" and compressed[feature][\"min\"] is None:\n",
"\t\tif operation == \"<=\" and compressed[feature][\"max\"] is None:\n",
"\t\t\tcompressed[feature][\"max\"] = value\n",
"\t\telif operation == \">\" and compressed[feature][\"max\"] is None:\n",
"\t\telif operation == \">\" and compressed[feature][\"min\"] is None:\n",
"\t\t\tcompressed[feature][\"min\"] = value\n",
"\t\telif operation == \"<=\" and value < compressed[feature][\"min\"]:\n",
"\t\telif operation == \"<=\" and value < compressed[feature][\"max\"]:\n",
"\t\t\tcompressed[feature][\"max\"] = value\n",
"\t\telif operation == \">\" and value > compressed[feature][\"max\"]:\n",
"\t\telif operation == \">\" and value > compressed[feature][\"min\"]:\n",
"\t\t\tcompressed[feature][\"min\"] = value\n",
"\n",
"\tpath[\"compressed\"] = compressed"
@@ -123,7 +123,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 78,
"id": "b6fbadbf",
"metadata": {},
"outputs": [],
@@ -183,7 +183,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 79,
"id": "0a767971",
"metadata": {},
"outputs": [],
@@ -213,16 +213,22 @@
"\tcompressed_layers[feature_name].append({\"min\": lower, \"max\": upper, \"paths\": paths, \"classes\": classes})\n",
"\t#print(\"=\"*40)\n",
"\n",
"path_to_class = {}\n",
"for i in range(len(tree[\"paths\"])):\n",
" path = tree[\"paths\"][i]\n",
" path_to_class[path[\"id\"]] = path[\"classification\"]\n",
"\n",
"compressed_tree = {\n",
"\t\"paths\": path_ids,\n",
"\t\"classes\": path_classes,\n",
"\t\"layers\": compressed_layers,\n",
" \"path_to_class\": path_to_class,\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 80,
"id": "561b0bc1",
"metadata": {},
"outputs": [],
@@ -241,7 +247,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "switch",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -255,7 +261,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.7"
"version": "3.12.9"
}
},
"nbformat": 4,

22
TreeToRMT.ipynb
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@@ -117,8 +117,8 @@
"[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"id mapping: \n",
"[['dst_range', 'dst_meta'], ['src_range', 'src_meta'], ['protocl_range', 'protocl_meta'], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []]\n",
"TCAM bits: 13312\n",
"RAM bits: 522\n"
"TCAM bits: 13184\n",
"RAM bits: 504\n"
]
}
],
@@ -263,8 +263,8 @@
"[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"id mapping: \n",
"[['dst_range', 'dst_meta'], ['src_range', 'src_meta'], ['protocl_range', 'protocl_meta'], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []]\n",
"TCAM bits: 3584\n",
"RAM bits: 522\n"
"TCAM bits: 3320\n",
"RAM bits: 504\n"
]
}
],
@@ -274,6 +274,14 @@
"print(f\"RAM bits: {ram_bits}\")"
]
},
{
"cell_type": "markdown",
"id": "2504b1ba",
"metadata": {},
"source": [
"# Priority Aware Prefix Expansion"
]
},
{
"cell_type": "code",
"execution_count": 9,
@@ -368,8 +376,8 @@
"[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"id mapping: \n",
"[['dst_range', 'dst_meta'], ['src_range', 'src_meta'], ['protocl_range', 'protocl_meta'], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []]\n",
"TCAM bits: 2120\n",
"RAM bits: 522\n"
"TCAM bits: 2152\n",
"RAM bits: 504\n"
]
}
],
@@ -382,7 +390,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "switch",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},

74
combine.py Normal file
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@@ -0,0 +1,74 @@
#!/usr/bin/env python3
"""combined.py
Concatenate every CSV that matches the pattern
data/processed/<name>/<name>.csv
into a single file:
data/combined/data.csv
The script streams each source CSV in 1‑Mio‑row chunks so memory stays low.
Typos in the historic column names (protocl/classfication) are fixed on‑the‑fly.
Usage
-----
python combined.py
You can optionally supply a different root directory:
python combined.py --root other/processed_dir --out other/combined/data.csv
"""
from __future__ import annotations
import argparse
from pathlib import Path
import os
import pandas as pd
CHUNK = 1_000_000 # rows per read_csv chunk
def fix_cols(df: pd.DataFrame) -> pd.DataFrame:
"""Rename legacy columns to canonical names."""
return df.rename(
columns={"protocl": "protocol", "classfication": "classification"}
)
def find_source_csvs(proc_root: Path):
"""Yield CSV paths that exactly match processed/<name>/<name>.csv."""
for sub in sorted(proc_root.iterdir()):
if not sub.is_dir():
continue
target = sub / f"{sub.name}.csv"
if target.exists():
yield target
def combine(proc_root: Path, out_path: Path):
out_path.parent.mkdir(parents=True, exist_ok=True)
first_write = True
for csv_path in find_source_csvs(proc_root):
print(f"→ adding {csv_path.relative_to(proc_root.parent)}")
for chunk in pd.read_csv(csv_path, chunksize=CHUNK):
chunk = fix_cols(chunk)
chunk.to_csv(
out_path,
mode="w" if first_write else "a",
header=first_write,
index=False,
)
first_write = False
print(f"✓ combined CSV written to {out_path}")
def main():
p = argparse.ArgumentParser(description="Combine processed CSVs into one.")
p.add_argument("--root", default="data/processed", help="processed dir root")
p.add_argument("--out", default="data/combined/data.csv", help="output CSV")
args = p.parse_args()
combine(Path(args.root).expanduser(), Path(args.out).expanduser())
if __name__ == "__main__":
main()

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example/compressed_tree.json Normal file
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@@ -0,0 +1,560 @@
{
"paths": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22
],
"classes": [
"Amazon Echo",
"Belkin Motion Sensor",
"Belkin Switch",
"Dropcam",
"HP Printer",
"LiFX Bulb",
"NEST Smoke Sensor",
"Netatmo Camera",
"Netatmo Weather station",
"Pixstart photo frame",
"Samsung Smart Cam",
"Smart Things",
"TP-Link Camera",
"TP-Link Plug",
"Triby Speaker",
"Withings",
"Withings Scale",
"Withings sleep sensor",
"iHome PowerPlug",
"other"
],
"layers": {
"dst": [
{
"min": null,
"max": 2136,
"paths": [
0,
1,
2,
3,
4,
5,
6
],
"classes": [
8,
19,
4
]
},
{
"min": 2136,
"max": 2224,
"paths": [
7
],
"classes": [
11
]
},
{
"min": 2224,
"max": 5016,
"paths": [
8,
9
],
"classes": [
1,
19
]
},
{
"min": 5016,
"max": 25848,
"paths": [
10,
11,
12
],
"classes": [
19,
7
]
},
{
"min": 25848,
"max": 47936,
"paths": [
10,
11,
13
],
"classes": [
19,
7
]
},
{
"min": 47936,
"max": 47944,
"paths": [
14
],
"classes": [
3
]
},
{
"min": 47944,
"max": 49152,
"paths": [
16,
15
],
"classes": [
10,
7
]
},
{
"min": 49152,
"max": 49160,
"paths": [
17,
18
],
"classes": [
16,
2
]
},
{
"min": 49160,
"max": null,
"paths": [
19,
20,
21,
22
],
"classes": [
17,
19,
15
]
}
],
"src": [
{
"min": null,
"max": 64,
"paths": [
0,
1,
7,
8,
10,
14,
15,
17,
19
],
"classes": [
3,
7,
11,
15,
16,
19
]
},
{
"min": 64,
"max": 128,
"paths": [
3,
5,
7,
8,
10,
14,
15,
17,
19
],
"classes": [
3,
4,
7,
11,
15,
16,
19
]
},
{
"min": 128,
"max": 280,
"paths": [
3,
6,
7,
8,
10,
14,
15,
17,
19
],
"classes": [
3,
4,
7,
11,
15,
16,
19
]
},
{
"min": 280,
"max": 816,
"paths": [
3,
6,
7,
8,
11,
14,
15,
17,
19
],
"classes": [
3,
4,
7,
11,
15,
16,
19
]
},
{
"min": 816,
"max": 1576,
"paths": [
4,
6,
7,
8,
11,
14,
15,
17,
19
],
"classes": [
3,
7,
11,
15,
16,
19
]
},
{
"min": 1576,
"max": 2488,
"paths": [
4,
6,
7,
8,
11,
14,
15,
18,
19
],
"classes": [
2,
3,
7,
11,
15,
19
]
},
{
"min": 2488,
"max": 4776,
"paths": [
4,
6,
7,
8,
11,
14,
16,
18,
19
],
"classes": [
2,
3,
7,
10,
11,
15,
19
]
},
{
"min": 4776,
"max": 5224,
"paths": [
4,
6,
7,
8,
11,
14,
16,
18,
20
],
"classes": [
2,
3,
7,
10,
11,
17,
19
]
},
{
"min": 5224,
"max": 9048,
"paths": [
4,
6,
7,
8,
12,
13,
14,
16,
18,
20
],
"classes": [
2,
3,
10,
11,
17,
19
]
},
{
"min": 9048,
"max": 43008,
"paths": [
4,
6,
7,
8,
12,
13,
14,
16,
18,
21
],
"classes": [
2,
3,
10,
11,
19
]
},
{
"min": 43008,
"max": 50384,
"paths": [
4,
6,
7,
9,
12,
13,
14,
16,
18,
21
],
"classes": [
1,
2,
3,
10,
11,
19
]
},
{
"min": 50384,
"max": null,
"paths": [
4,
6,
7,
9,
12,
13,
14,
16,
18,
22
],
"classes": [
1,
2,
3,
10,
11,
19
]
}
],
"protocl": [
{
"min": null,
"max": 0,
"paths": [
0,
2,
3,
4,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22
],
"classes": [
1,
2,
3,
4,
7,
8,
10,
11,
15,
16,
17,
19
]
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"feature": "dst",
"operation": "<=",
"value": 47936.0
},
{
"feature": "dst",
"operation": "<=",
"value": 2128.0
},
{
"feature": "src",
"operation": "<=",
"value": 64.0
},
{
"feature": "src",
"operation": "<=",
"value": 64.0
},
{
"feature": "protocl",
"operation": "<=",
"value": 0.0
}
],
"classification": 19,
"id": 0
},
{
"conditions": [
{
"feature": "dst",
"operation": "<=",
"value": 47936.0
},
{
"feature": "dst",
"operation": "<=",
"value": 2128.0
},
{
"feature": "src",
"operation": "<=",
"value": 64.0
},
{
"feature": "src",
"operation": "<=",
"value": 64.0
},
{
"feature": "protocl",
"operation": ">",
"value": 0.0
}
],
"classification": 19,
"id": 1
},
{
"conditions": [
{
"feature": "dst",
"operation": "<=",
"value": 47936.0
},
{
"feature": "dst",
"operation": "<=",
"value": 2128.0
},
{
"feature": "src",
"operation": "<=",
"value": 64.0
},
{
"feature": "src",
"operation": ">",
"value": 64.0
}
],
"classification": 8,
"id": 2
},
{
"conditions": [
{
"feature": "dst",
"operation": "<=",
"value": 47936.0
},
{
"feature": "dst",
"operation": "<=",
"value": 2128.0
},
{
"feature": "src",
"operation": ">",
"value": 64.0
},
{
"feature": "protocl",
"operation": "<=",
"value": 0.0
},
{
"feature": "src",
"operation": "<=",
"value": 816.0
}
],
"classification": 4,
"id": 3
},
{
"conditions": [
{
"feature": "dst",
"operation": "<=",
"value": 47936.0
},
{
"feature": "dst",
"operation": "<=",
"value": 2128.0
},
{
"feature": "src",
"operation": ">",
"value": 64.0
},
{
"feature": "protocl",
"operation": "<=",
"value": 0.0
},
{
"feature": "src",
"operation": ">",
"value": 816.0
}
],
"classification": 19,
"id": 4
},
{
"conditions": [
{
"feature": "dst",
"operation": "<=",
"value": 47936.0
},
{
"feature": "dst",
"operation": "<=",
"value": 2128.0
},
{
"feature": "src",
"operation": ">",
"value": 64.0
},
{
"feature": "protocl",
"operation": ">",
"value": 0.0
},
{
"feature": "src",
"operation": "<=",
"value": 128.0
}
],
"classification": 19,
"id": 5
},
{
"conditions": [
{
"feature": "dst",
"operation": "<=",
"value": 47936.0
},
{
"feature": "dst",
"operation": "<=",
"value": 2128.0
},
{
"feature": "src",
"operation": ">",
"value": 64.0
},
{
"feature": "protocl",
"operation": ">",
"value": 0.0
},
{
"feature": "src",
"operation": ">",
"value": 128.0
}
],
"classification": 19,
"id": 6
},
{
"conditions": [
{
"feature": "dst",
"operation": "<=",
"value": 47936.0
},
{
"feature": "dst",
"operation": ">",
"value": 2128.0
},
{
"feature": "dst",
"operation": "<=",
"value": 5024.0
},
{
"feature": "dst",
"operation": "<=",
"value": 2224.0
}
],
"classification": 11,
"id": 7
},
{
"conditions": [
{
"feature": "dst",
"operation": "<=",
"value": 47936.0
},
{
"feature": "dst",
"operation": ">",
"value": 2128.0
},
{
"feature": "dst",
"operation": "<=",
"value": 5024.0
},
{
"feature": "dst",
"operation": ">",
"value": 2224.0
},
{
"feature": "src",
"operation": "<=",
"value": 43008.0
}
],
"classification": 19,
"id": 8
},
{
"conditions": [
{
"feature": "dst",
"operation": "<=",
"value": 47936.0
},
{
"feature": "dst",
"operation": ">",
"value": 2128.0
},
{
"feature": "dst",
"operation": "<=",
"value": 5024.0
},
{
"feature": "dst",
"operation": ">",
"value": 2224.0
},
{
"feature": "src",
"operation": ">",
"value": 43008.0
}
],
"classification": 1,
"id": 9
},
{
"conditions": [
{
"feature": "dst",
"operation": "<=",
"value": 47936.0
},
{
"feature": "dst",
"operation": ">",
"value": 2128.0
},
{
"feature": "dst",
"operation": ">",
"value": 5024.0
},
{
"feature": "src",
"operation": "<=",
"value": 5232.0
},
{
"feature": "src",
"operation": "<=",
"value": 288.0
}
],
"classification": 19,
"id": 10
},
{
"conditions": [
{
"feature": "dst",
"operation": "<=",
"value": 47936.0
},
{
"feature": "dst",
"operation": ">",
"value": 2128.0
},
{
"feature": "dst",
"operation": ">",
"value": 5024.0
},
{
"feature": "src",
"operation": "<=",
"value": 5232.0
},
{
"feature": "src",
"operation": ">",
"value": 288.0
}
],
"classification": 7,
"id": 11
},
{
"conditions": [
{
"feature": "dst",
"operation": "<=",
"value": 47936.0
},
{
"feature": "dst",
"operation": ">",
"value": 2128.0
},
{
"feature": "dst",
"operation": ">",
"value": 5024.0
},
{
"feature": "src",
"operation": ">",
"value": 5232.0
},
{
"feature": "dst",
"operation": "<=",
"value": 25856.0
}
],
"classification": 19,
"id": 12
},
{
"conditions": [
{
"feature": "dst",
"operation": "<=",
"value": 47936.0
},
{
"feature": "dst",
"operation": ">",
"value": 2128.0
},
{
"feature": "dst",
"operation": ">",
"value": 5024.0
},
{
"feature": "src",
"operation": ">",
"value": 5232.0
},
{
"feature": "dst",
"operation": ">",
"value": 25856.0
}
],
"classification": 19,
"id": 13
},
{
"conditions": [
{
"feature": "dst",
"operation": ">",
"value": 47936.0
},
{
"feature": "dst",
"operation": "<=",
"value": 47936.0
}
],
"classification": 3,
"id": 14
},
{
"conditions": [
{
"feature": "dst",
"operation": ">",
"value": 47936.0
},
{
"feature": "dst",
"operation": ">",
"value": 47936.0
},
{
"feature": "dst",
"operation": "<=",
"value": 49168.0
},
{
"feature": "dst",
"operation": "<=",
"value": 49152.0
},
{
"feature": "src",
"operation": "<=",
"value": 2480.0
}
],
"classification": 7,
"id": 15
},
{
"conditions": [
{
"feature": "dst",
"operation": ">",
"value": 47936.0
},
{
"feature": "dst",
"operation": ">",
"value": 47936.0
},
{
"feature": "dst",
"operation": "<=",
"value": 49168.0
},
{
"feature": "dst",
"operation": "<=",
"value": 49152.0
},
{
"feature": "src",
"operation": ">",
"value": 2480.0
}
],
"classification": 10,
"id": 16
},
{
"conditions": [
{
"feature": "dst",
"operation": ">",
"value": 47936.0
},
{
"feature": "dst",
"operation": ">",
"value": 47936.0
},
{
"feature": "dst",
"operation": "<=",
"value": 49168.0
},
{
"feature": "dst",
"operation": ">",
"value": 49152.0
},
{
"feature": "src",
"operation": "<=",
"value": 1584.0
}
],
"classification": 16,
"id": 17
},
{
"conditions": [
{
"feature": "dst",
"operation": ">",
"value": 47936.0
},
{
"feature": "dst",
"operation": ">",
"value": 47936.0
},
{
"feature": "dst",
"operation": "<=",
"value": 49168.0
},
{
"feature": "dst",
"operation": ">",
"value": 49152.0
},
{
"feature": "src",
"operation": ">",
"value": 1584.0
}
],
"classification": 2,
"id": 18
},
{
"conditions": [
{
"feature": "dst",
"operation": ">",
"value": 47936.0
},
{
"feature": "dst",
"operation": ">",
"value": 47936.0
},
{
"feature": "dst",
"operation": ">",
"value": 49168.0
},
{
"feature": "src",
"operation": "<=",
"value": 9040.0
},
{
"feature": "src",
"operation": "<=",
"value": 4784.0
}
],
"classification": 15,
"id": 19
},
{
"conditions": [
{
"feature": "dst",
"operation": ">",
"value": 47936.0
},
{
"feature": "dst",
"operation": ">",
"value": 47936.0
},
{
"feature": "dst",
"operation": ">",
"value": 49168.0
},
{
"feature": "src",
"operation": "<=",
"value": 9040.0
},
{
"feature": "src",
"operation": ">",
"value": 4784.0
}
],
"classification": 17,
"id": 20
},
{
"conditions": [
{
"feature": "dst",
"operation": ">",
"value": 47936.0
},
{
"feature": "dst",
"operation": ">",
"value": 47936.0
},
{
"feature": "dst",
"operation": ">",
"value": 49168.0
},
{
"feature": "src",
"operation": ">",
"value": 9040.0
},
{
"feature": "src",
"operation": "<=",
"value": 50384.0
}
],
"classification": 19,
"id": 21
},
{
"conditions": [
{
"feature": "dst",
"operation": ">",
"value": 47936.0
},
{
"feature": "dst",
"operation": ">",
"value": 47936.0
},
{
"feature": "dst",
"operation": ">",
"value": 49168.0
},
{
"feature": "src",
"operation": ">",
"value": 9040.0
},
{
"feature": "src",
"operation": ">",
"value": 50384.0
}
],
"classification": 19,
"id": 22
}
],
"classes": [
"Amazon Echo",
"Belkin Motion Sensor",
"Belkin Switch",
"Dropcam",
"HP Printer",
"LiFX Bulb",
"NEST Smoke Sensor",
"Netatmo Camera",
"Netatmo Weather station",
"Pixstart photo frame",
"Samsung Smart Cam",
"Smart Things",
"TP-Link Camera",
"TP-Link Plug",
"Triby Speaker",
"Withings",
"Withings Scale",
"Withings sleep sensor",
"iHome PowerPlug",
"other"
]
}

47
example/worst_case_rmt.json Normal file
View File

@@ -0,0 +1,47 @@
[
{
"id": "dst_range",
"step": 0,
"match": "ternary",
"entries": 288,
"key_size": 16
},
{
"id": "dst_meta",
"step": 0,
"match": "exact",
"method": "index",
"key_size": 4,
"data_size": 20
},
{
"id": "src_range",
"step": 1,
"match": "ternary",
"entries": 384,
"key_size": 16
},
{
"id": "src_meta",
"step": 1,
"match": "exact",
"method": "index",
"key_size": 4,
"data_size": 20
},
{
"id": "protocl_range",
"step": 2,
"match": "ternary",
"entries": 32,
"key_size": 8
},
{
"id": "protocl_meta",
"step": 2,
"match": "exact",
"method": "index",
"key_size": 1,
"data_size": 20
}
]

80
extract_all_datasets.py Normal file
View File

@@ -0,0 +1,80 @@
#!/usr/bin/env python3
from pathlib import Path
import numpy as np
import pandas as pd
from labels import mac_to_label
from tqdm import tqdm
import os
ROOT = Path(__file__).resolve().parent
PCAP_DIR = ROOT / "data" / "pcap"
CSV_DIR = ROOT / "data" / "processed"
CSV_DIR.mkdir(parents=True, exist_ok=True)
BATCH = 100_000 # packets per chunk
from scapy.all import rdpcap
def process_pcap(pcap_path: str, csv_path: str) -> None:
all_packets = rdpcap(pcap_path)
print("rdpcap done", flush=True)
results = []
for packet in tqdm(all_packets):
size = len(packet)
try:
proto = packet.proto
except AttributeError:
proto = 0
try:
sport = packet.sport
dport = packet.dport
except AttributeError:
sport = 0
dport = 0
proto = int(proto)
sport = int(sport)
dport = int(dport)
if "Ether" in packet:
eth_dst = packet["Ether"].dst
if eth_dst in mac_to_label:
classification = mac_to_label[eth_dst]
else:
classification = "other"
else:
classification = "other"
metric = [proto,sport,dport,classification]
results.append(metric)
results = (np.array(results)).T
# store the features in the dataframe
dataframe = pd.DataFrame({'protocl':results[0],'src':results[1],'dst':results[2],'classfication':results[3]})
columns = ['protocl','src','dst','classfication']
# save the dataframe to the csv file, if not exsit, create one.
if os.path.exists(csv_path):
dataframe.to_csv(csv_path,index=False,sep=',',mode='a',columns = columns, header=False)
else:
dataframe.to_csv(csv_path,index=False,sep=',',columns = columns)
print("Done")
def main() -> None:
for pcap in sorted(PCAP_DIR.rglob("*.pcap")):
rel_csv = pcap.relative_to(PCAP_DIR).with_suffix(".csv")
csv_path = CSV_DIR / rel_csv
if csv_path.exists():
print(f"Skip {rel_csv} (CSV exists)")
continue
print(f"Processing {rel_csv}")
csv_path.parent.mkdir(parents=True, exist_ok=True)
process_pcap(str(pcap), str(csv_path))
if __name__ == "__main__":
main()

50
extract_tars.sh Normal file
View File

@@ -0,0 +1,50 @@
#!/usr/bin/env bash
# Usage: extract_all.sh SOURCE_DIR TARGET_DIR
# For every .tar, .tar.gz, .tgz, .tar.bz2, .tar.xz in SOURCE_DIR:
# 1. Create TARGET_DIR/<name>/
# 2. If TARGET_DIR/<name>/<name>.pcap already exists, skip the archive.
# 3. Otherwise, extract the archive into its own folder.
set -euo pipefail
if [[ $# -ne 2 ]]; then
echo "Usage: $0 SOURCE_DIR TARGET_DIR" >&2
exit 1
fi
src_dir="$1"
dst_dir="$2"
mkdir -p "$dst_dir"
# Strip common extensions to recover the base name
strip_ext() {
local n="$1"
n=${n%.tar.gz}; n=${n%.tgz}; n=${n%.tar.bz2}; n=${n%.tar.xz}; n=${n%.tar}
echo "$n"
}
shopt -s nullglob
for archive in "$src_dir"/*.tar{,.gz,.bz2,.xz} "$src_dir"/*.tgz; do
base=$(basename "$archive")
name=$(strip_ext "$base")
out_dir="$dst_dir/$name"
key_file="$out_dir/$name.pcap"
if [[ -f "$key_file" ]]; then
echo "Skipping $archive — $key_file already present"
continue
fi
echo "Extracting $archive into $out_dir"
mkdir -p "$out_dir"
case "$archive" in
*.tar) tar -xf "$archive" -C "$out_dir" ;;
*.tar.gz|*.tgz) tar -xzf "$archive" -C "$out_dir" ;;
*.tar.bz2) tar -xjf "$archive" -C "$out_dir" ;;
*.tar.xz) tar -xJf "$archive" -C "$out_dir" ;;
*) echo "Unknown type: $archive" ;;
esac
done
echo "All archives processed."

3
requirements.txt
View File

@@ -3,4 +3,5 @@ numpy
pandas
scikit-learn
pydotplus
matplotlib
matplotlib
scipy

44
sanity_check/csvdiff.py Normal file
View File

@@ -0,0 +1,44 @@
#!/usr/bin/env python3
"""
csvdiff.py file1.csv file2.csv
Streams both files; prints the first differing line or
‘No differences found’. Uses O(1) memory.
"""
import sys
from itertools import zip_longest
from pathlib import Path
def open_checked(p: str):
print(p)
path = Path(p)
try:
return path.open("r", newline=""), path
except FileNotFoundError:
sys.exit(f"Error: {path} not found")
def human(n: int) -> str:
return f"{n:,}"
def main(a_path: str, b_path: str) -> None:
fa, a = open_checked(a_path)
fb, b = open_checked(b_path)
with fa, fb:
for idx, (ra, rb) in enumerate(zip_longest(fa, fb), 1):
if ra != rb:
print(f"Files differ at line {human(idx)}")
if ra is None:
print(f"{a} ended early")
elif rb is None:
print(f"{b} ended early")
else:
print(f"{a}: {ra.rstrip()}")
print(f"{b}: {rb.rstrip()}")
return
print("No differences found")
if __name__ == "__main__":
if len(sys.argv) != 3:
sys.exit("Usage: csvdiff.py file1.csv file2.csv")
main(sys.argv[1], sys.argv[2])

600
sanity_check/data_visualization.ipynb Normal file
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File diff suppressed because one or more lines are too long

206
sanity_check/diversity_metrics.py Normal file
View File

@@ -0,0 +1,206 @@
#!/usr/bin/env python3
"""diversity_metrics.py (fast version)
Estimate how much diversity each CSV adds without building a giant in‑memory
DataFrame. Designed for IoT packet logs with millions of rows.
Quick summary printed as a GitHub‑style table (requires *tabulate*; falls back
to pandas plain text).
Usage
-----
python diversity_metrics.py path/to/processed_dir [-r] [--sample 50000]
Metrics
-------
ΔEntropy : change in Shannon entropy of *classification* counts
ΔGini : change in Gini impurity of the same counts
χ² p : Pearson χ² p‑value old vs new classification counts
Jaccard : similarity of unique (src,dst) pairs (0 → new pairs, 1 → no new)
KS src p : Kolmogorov–Smirnov p‑value, source‑port dist (uses sampling)
KS dst p : Kolmogorov–Smirnov p‑value, dest‑port dist (uses sampling)
Speed tricks
------------
* No growing DataFrame; we keep Counters / sets / lists.
* Ports for KS are *sampled* (default 50 k) to bound cost.
* (src,dst) pairs are hashed to a 32‑bit int to reduce set overhead.
* pandas reads via **pyarrow** engine when available.
"""
import argparse
from pathlib import Path
from collections import Counter
from typing import List, Set
import numpy as np
import pandas as pd
from scipy.stats import chi2_contingency, ks_2samp, entropy
try:
from tabulate import tabulate
_USE_TABULATE = True
except ImportError:
_USE_TABULATE = False
# -----------------------------------------------------------------------------
# Helper metrics
# -----------------------------------------------------------------------------
def shannon(counts: Counter) -> float:
total = sum(counts.values())
if total == 0:
return 0.0
p = np.fromiter(counts.values(), dtype=float)
p /= total
return entropy(p, base=2)
def gini(counts: Counter) -> float:
total = sum(counts.values())
if total == 0:
return 0.0
return 1.0 - sum((n / total) ** 2 for n in counts.values())
def jaccard(a: Set[int], b: Set[int]) -> float:
if not a and not b:
return 1.0
return len(a & b) / len(a | b)
# -----------------------------------------------------------------------------
# Core analysis
# -----------------------------------------------------------------------------
def analyse(csv_files: List[Path], sample_size: int):
"""Return list of dicts with diversity metrics for each added file."""
# cumulative state (no big DataFrame!)
class_counter: Counter = Counter()
pair_hashes: Set[int] = set()
src_list: List[int] = []
dst_list: List[int] = []
rows = []
for csv_path in csv_files:
df = pd.read_csv(
csv_path,
engine="pyarrow" if pd.__version__ >= "2" else "c", # fast parse
usecols=["protocl", "src", "dst", "classfication"],
dtype={
"protocl": "uint16",
"protocol": "uint16",
"src": "uint16",
"dst": "uint16",
},
)
# normalise column names
df.rename(columns={"protocl": "protocol", "classfication": "classification"}, inplace=True)
# snapshot previous state
prev_class = class_counter.copy()
prev_pairs = pair_hashes.copy()
prev_src = np.asarray(src_list, dtype=np.uint16)
prev_dst = np.asarray(dst_list, dtype=np.uint16)
# --- update cumulative structures ------------------------------------
class_counter.update(df["classification"].value_counts().to_dict())
# hash (src,dst) into 32‑bit int to save memory
pair_ids = (df["src"].to_numpy(dtype=np.uint32) << np.uint32(16)) | \
df["dst"].to_numpy(dtype=np.uint32)
# extend port lists (keep small ints)
src_list.extend(df["src"].tolist())
dst_list.extend(df["dst"].tolist())
# --- metrics ----------------------------------------------------------
# χ² classification
chi_p = np.nan
if prev_class:
all_classes = list(set(prev_class) | set(df["classification"].unique()))
old = [prev_class.get(c, 0) for c in all_classes]
new = [df["classification"].value_counts().get(c, 0) for c in all_classes]
_, chi_p, _, _ = chi2_contingency([old, new])
# entropy & gini deltas
delta_entropy = shannon(class_counter) - shannon(prev_class)
delta_gini = gini(class_counter) - gini(prev_class)
# Jaccard on pair hashes
jc = jaccard(prev_pairs, pair_hashes)
# KS tests on sampled ports
ks_src_p = ks_dst_p = np.nan
if prev_src.size:
new_src = df["src"].to_numpy(dtype=np.uint16)
new_dst = df["dst"].to_numpy(dtype=np.uint16)
if prev_src.size > sample_size:
prev_src_sample = np.random.choice(prev_src, sample_size, replace=False)
else:
prev_src_sample = prev_src
if new_src.size > sample_size:
new_src_sample = np.random.choice(new_src, sample_size, replace=False)
else:
new_src_sample = new_src
if prev_dst.size > sample_size:
prev_dst_sample = np.random.choice(prev_dst, sample_size, replace=False)
else:
prev_dst_sample = prev_dst
if new_dst.size > sample_size:
new_dst_sample = np.random.choice(new_dst, sample_size, replace=False)
else:
new_dst_sample = new_dst
ks_src_p = ks_2samp(prev_src_sample, new_src_sample).pvalue
ks_dst_p = ks_2samp(prev_dst_sample, new_dst_sample).pvalue
rows.append(
{
"File": csv_path.name,
"Rows": len(df),
"ΔEntropy": round(delta_entropy, 4),
"ΔGini": round(delta_gini, 4),
"χ² p": f"{chi_p:.3g}" if not np.isnan(chi_p) else "NA",
"Jaccard": round(jc, 3),
"KS src p": f"{ks_src_p:.3g}" if not np.isnan(ks_src_p) else "NA",
"KS dst p": f"{ks_dst_p:.3g}" if not np.isnan(ks_dst_p) else "NA",
}
)
return rows
# -----------------------------------------------------------------------------
# CLI
# -----------------------------------------------------------------------------
def main():
ap = argparse.ArgumentParser(description="Evaluate diversity contribution of each CSV (fast version).")
ap.add_argument("csv_dir", help="Directory containing CSV files")
ap.add_argument("-r", "--recursive", action="store_true", help="Recursively search csv_dir")
ap.add_argument("--sample", type=int, default=50_000, help="Sample size for KS tests (default 50k)")
args = ap.parse_args()
root = Path(args.csv_dir)
pattern = "**/*.csv" if args.recursive else "*.csv"
csv_files = sorted(root.glob(pattern))
if not csv_files:
print("No CSV files found.")
return
table_rows = analyse(csv_files, args.sample)
if _USE_TABULATE:
print(tabulate(table_rows, headers="keys", tablefmt="github", floatfmt=".4f"))
else:
print(pd.DataFrame(table_rows).to_string(index=False))
print(
"\nLegend:\n • p-values (χ², KS) < 0.05 → new file significantly shifts distribution (GOOD)"
"\n • Positive ΔEntropy or ΔGini → richer mix; near 0 → little new info"
"\n • Jaccard close to 0 → many unseen (src,dst) pairs; close to 1 → redundant."
)
if __name__ == "__main__":
main()

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setup.sh Normal file
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#!/usr/bin/env bash
# Creates the directory layout:
# data/
# tar/
# pcap/
# processed/
set -euo pipefail
root="$(cd -- "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
mkdir -p "$root"/data/{tar,pcap,processed,combined}
echo "Directory structure ready under $root/data/"