IntruSense: Multimodal IoT Intrusion Detection System

This repository contains the implementation of IntruSense, a lightweight machine-learning–based intrusion detection system (IDS) for IoT environments.

Problem Statement

IoT and IIoT networks are vulnerable to attacks such as DoS, DDoS, and ransomware. Traditional IDS solutions are unsuitable due to resource constraints of IoT devices. This project explores machine-learning–based intrusion detection using multimodal data fusion.

Dataset

• TON-IoT Dataset (UNSW Canberra IoT Lab)
• Modalities used:
  • Network traffic
  • Linux / Windows system logs
  • Telemetry data

Dataset link:
https://research.unsw.edu.au/projects/toniot-datasets

Note: Due to size constraints, datasets are not included in this repository.

Methodology

1. Data preprocessing (cleaning, encoding, scaling)
2. Training per-modality ML models
3. Late-fusion using probability averaging
4. Performance evaluation using accuracy, precision, recall, F1-score

Models Used

• Logistic Regression
• Support Vector Machine (SVM)
• Random Forest (baseline)
• Fusion-based classifier

How to Run

pip install -r requirements.txt
python fus.py
# IntruSense

Results

All experimental results, confusion matrices, and evaluation metrics are available in the results/ and plots/ directories.