AI-Powered Intrusion Detection System for Home WiFi Networks

home WiFi networks monitors traffic in real time and uses machine learning to detect and block suspicious activity, protecting devices and personal data automatically

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Inspiration Home WiFi networks are becoming more vulnerable as more devices—phones, smart TVs, IoT gadgets—connect to them. Most home routers in India still rely on weak security features and offer no real-time intrusion detection or AI-based protection. We wanted to create a simple, affordable, and intelligent system that can detect suspicious activity automatically, protect families from cyber threats, and bring enterprise-level security to home networks.

What it does

Our AI-Powered Intrusion Detection System for Home WiFi Networks:

Monitors all network traffic in real time

Uses AI/ML to detect abnormal behavior or unknown attacks

Identifies rogue devices connecting to the WiFi

Generates alerts instantly (Telegram notifications / dashboard)

Auto-blocks suspicious MAC addresses (optional)

Provides a clean dashboard showing device activity, anomalies, and threat reports

Works even on low-cost hardware (Raspberry Pi / normal laptop)

How we built it

We followed a full end-to-end pipeline:

  1. Packet Capture Module

Used Scapy and Tshark to capture WiFi traffic (JSONL format).

Extracted metadata like MAC, length, protocol, timestamps, etc.

  1. Feature Extraction Engine

Converted raw packets into per-device behavior profiles using sliding windows.

Extracted features such as packet count, byte count, port variety, ARP frequency, deauth indicators, etc.

  1. AI Anomaly Detection Model

Trained an Isolation Forest on normal home WiFi traffic.

Classified unusual behavior as anomalies (scores).

  1. Detection Service

Runs every 10 seconds

Uses the AI model to detect anomalies

Sends suspicious events to the controller

  1. Alert + Auto-Response System

Built using FastAPI

Sends Telegram alerts

Optional router-based MAC blocking

  1. Dashboard (Frontend)

Built a simple web dashboard (Flask/React option)

Shows connected devices, live anomalies, and threat timeline.

  1. Demo

Showcased using synthetic abnormal traffic + replayed pcaps for safe testing.

Challenges we ran into

Getting stable packet captures across different WiFi cards and routers

Avoiding false positives (normal spikes in home traffic looked suspicious at first)

Training the model with clean “normal” data without noise

Ensuring the system works on low hardware like Raspberry Pi

Integration between modules (sensor → model → controller → dashboard)

Designing a dashboard that updates in real time without lag

Ensuring safe and ethical testing without actual harmful attacks

Accomplishments that we're proud of

Built a fully functional AI-powered IDS in a short time

Achieved real-time detection with low latency

Created a clean and understandable pipeline from capture → AI → alerts

Successfully detected abnormal traffic patterns during testing

Integrated Telegram alerts + optional MAC blocking

Designed a simple but powerful dashboard

Made the solution hardware-friendly so families can actually use it at home

Followed safe testing practices throughout

What we learned

How to process and analyze raw WiFi packets

How network anomalies differ from normal user behavior

How sliding-window feature engineering improves detection accuracy

How unsupervised ML models (like Isolation Forest) identify unknown threats

How to deploy ML models in real-time services

Building clean APIs with FastAPI

Creating lightweight dashboards for cybersecurity monitoring

The importance of ethical, safe cybersecurity experimentation

What's next for AI-Powered Intrusion Detection System for Home WiFi Networks

We plan to extend the project with:

🔹 Device Fingerprinting

Identify device types (phone, laptop, IoT) based on traffic patterns.

🔹 Deep Learning Model Upgrade

Replace IsolationForest with a deep autoencoder or graph neural network for better anomaly detection.

🔹 Mobile App

Build an Android/iOS companion app for instant notifications.

🔹 Cloud Threat Intelligence

Fetch known malicious IP/MAC databases for stronger detection.

🔹 Honeypot Mode

Create a decoy access point to trap and study attacker behavior.

🔹 Parental Monitoring

Detect risky browsing patterns for smart home safety.

🔹 Smart Router Integration

Direct integration with OpenWrt routers for seamless protection.

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