mirror of
https://github.com/ltcptgeneral/IdealRMT-DecisionTrees.git
synced 2025-09-04 06:17:24 +00:00
112 lines
2.8 KiB
Plaintext
112 lines
2.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "97e76d73",
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"metadata": {},
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"outputs": [],
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"source": [
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"from scapy.all import *\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import argparse\n",
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"import os\n",
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"from labels import mac_to_label\n",
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"\n",
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"inputfile = \"data.pcap\"\n",
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"outputfile = \"data.csv\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "119623a5",
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"metadata": {},
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"outputs": [],
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"source": [
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"#read the pcap file and extract the features for each packet\n",
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"all_packets = rdpcap(inputfile)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "f5584562",
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"metadata": {},
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"outputs": [],
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"source": [
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"results = []\n",
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"for packet in all_packets:\n",
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" size = len(packet)\n",
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" try:\n",
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" proto = packet.proto\n",
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" except AttributeError:\n",
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" proto = 0\n",
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" try:\n",
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" sport = packet.sport\n",
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" dport = packet.dport\n",
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" except AttributeError:\n",
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" sport = 0\n",
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" dport = 0\n",
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"\n",
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" proto = int(proto)\n",
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" sport = int(sport)\n",
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" dport = int(dport)\n",
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"\n",
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" if \"Ether\" in packet:\n",
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" eth_dst = packet[\"Ether\"].dst\n",
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" if eth_dst in mac_to_label:\n",
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" classification = mac_to_label[eth_dst]\n",
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" else:\n",
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" classification = \"other\"\n",
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" else:\n",
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" classification = \"other\"\n",
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"\n",
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" metric = [proto,sport,dport,classification]\n",
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" results.append(metric)\n",
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"results = (np.array(results)).T"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "2e04c2d1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# store the features in the dataframe\n",
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"dataframe = pd.DataFrame({'protocl':results[0],'src':results[1],'dst':results[2],'classfication':results[3]})\n",
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"columns = ['protocl','src','dst','classfication']\n",
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"\n",
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"# save the dataframe to the csv file, if not exsit, create one.\n",
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"if os.path.exists(outputfile):\n",
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" dataframe.to_csv(outputfile,index=False,sep=',',mode='a',columns = columns, header=False)\n",
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"else:\n",
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" dataframe.to_csv(outputfile,index=False,sep=',',columns = columns)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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