AI-Based Intrusion Detection System for IoT Networks

Problem statement

IoT devices are often deployed with weak security and limited processing power, making them easy targets for attackers. Traditional firewalls and rule-based intrusion detection systems may not detect new or evolving attack patterns. There is a need for a lightweight, intelligent intrusion detection system that can analyze IoT network traffic and detect anomalies or attacks in real time.

Abstract

This project designs and implements an AI-based Intrusion Detection System (IDS) for IoT environments. Network traffic data such as packet size, protocol type, port usage and connection frequency is collected and transformed into feature vectors. Machine learning models like Random Forest, XGBoost, or Neural Networks are trained on labeled normal and attack traffic datasets (e.g., DoS, port scanning, botnet activity). During deployment, the trained model classifies incoming traffic as normal or suspicious and raises alerts when malicious behavior is detected. A dashboard displays real-time statistics and detected intrusion events.

Components required

• PC or server for training and inference
• Packet capture tool (Wireshark / tcpdump / custom sniffer)
• Python with Scikit-Learn / PyTorch / TensorFlow
• IoT network traffic dataset (e.g., Bot-IoT, UNSW-NB15, CIC-IDS)
• Local database or log files for event storage
• Web dashboard (Flask / Django / React)

Block diagram

Working

Network traffic from IoT devices and gateways is mirrored or captured at a monitoring node. A feature extraction module converts raw packets into numerical features such as connection duration, protocol, flag counts and packet statistics. These features are fed into a trained ML model which outputs a classification for each flow: normal or one of several attack types. If malicious activity is detected, the system logs the event with timestamp, source/destination IP and attack type, and raises an alert on the dashboard. Optionally, the IDS can signal a firewall or SDN controller to block the suspicious source automatically.

Applications

• Security monitoring for smart homes and smart campuses
• Industrial IoT networks in factories and plants
• Testbed for research on anomaly detection in IoT
• Educational platform for cybersecurity and ML integration