AI-Based Lightweight IDS for IoT Networks

1. Abstract

The rapid proliferation of Internet of Things (IoT) devices in critical sectors such as healthcare, smart homes, and industrial automation has introduced significant security vulnerabilities. Traditional Intrusion Detection Systems (IDS) are often too resource-intensive to be deployed directly onto IoT infrastructure. This project proposes the design and development of a lightweight, AI-powered IDS capable of operating within the stringent power, memory, and processing constraints of IoT environments. By leveraging optimized machine learning models and edge computing paradigms, the system aims to provide real-time, accurate threat detection while maintaining minimal computational overhead.

2. Introduction and Background

IoT networks consist of heterogeneous devices with limited battery life and processing power. Consequently, they are prime targets for cyberattacks, including Distributed Denial of Service (DDoS), Man-in-the-Middle (MitM), and ransomware. Conventional security solutions often create a bottleneck by sending all data to the cloud for analysis, leading to latency and privacy concerns.

This project addresses the critical need for "security at the edge." By embedding intelligence directly into the network layer or on edge gateways, the proposed IDS can detect anomalies locally, ensuring rapid response times and preserving network bandwidth.

3. Problem Statement

· Resource Constraints: Existing deep learning models are too heavy for IoT devices (limited RAM/CPU).

· Latency Issues: Cloud-based threat analysis introduces delays that are unacceptable for real-time IoT applications (e.g., autonomous vehicles).

· Evolving Threat Landscape: Signature-based IDS fail to detect zero-day attacks or polymorphic malware.

· False Positives: High rates of false alarms in traditional systems lead to alert fatigue and reduced trust in security mechanisms.

4. Proposed Solution

We propose a hybrid IDS framework that operates at the network edge (e.g., on a smart home hub or fog node). The solution combines feature engineering with a lightweight AI model to classify network traffic as benign or malicious.

Core Components:

1. Data Acquisition Module: Captures raw network traffic (pcap data) from the IoT environment.

2. Preprocessing & Feature Extraction: Cleans the data and extracts relevant flow-based features (packet size, protocol, inter-arrival time) to reduce dimensionality.

3. Lightweight AI Engine:

· Algorithm: Utilizes optimized models such as Decision Trees, Tiny Neural Networks (TinyML), or 1D Convolutional Neural Networks (CNNs).

· Optimization: Implements techniques like pruning and quantization to shrink the model size without sacrificing accuracy.

4. Classification & Alerting Module: Classifies traffic in real-time and triggers alerts for malicious activity.

5. Methodology

A. Dataset

· Training Data: Publicly available datasets such as CICIDS2017, NSL-KDD, or Bot-IoT will be used for training and validation.

· Simulation: A physical testbed comprising Raspberry Pis, ESP32s, and smart sensors will be used to simulate normal and attack traffic.

B. Feature Selection

To ensure lightweight operation, the system will select a minimal set of high-impact features (e.g., Flow Duration, Total Fwd Packets, Packet Length Mean) rather than processing all 80+ network features, thereby reducing computational load.

C. Model Training

· Phase 1: Train multiple models (Random Forest, SVM, CNN) on a high-performance server.

· Phase 2: Apply model compression techniques to convert the heavy model into a lightweight version suitable for embedded hardware.

· Phase 3: Deploy the compressed model onto edge devices using frameworks like TensorFlow Lite Micro.

D. Evaluation Metrics

The system will be evaluated based on:

· Accuracy & Detection Rate (DR)

· False Positive Rate (FPR)

· Computational Overhead: CPU usage, memory footprint, and energy consumption on the target IoT device.

· Inference Time: Time taken to classify a single packet or flow.

6. Expected Outcomes

· A functional prototype of a lightweight IDS running on a low-power device (e.g., Raspberry Pi).

· A comparative analysis showing the trade-off between model accuracy and resource consumption.

· A framework that can detect at least 5 types of IoT-specific attacks (e.g., Mirai Botnet, ARP Spoofing, TCP SYN Flood).

· Published results demonstrating that the system achieves >95% detection accuracy with <10% CPU usage on the target hardware.

7. Applications

· Smart Homes: Securing Wi-Fi routers and smart hubs.

· Healthcare: Protecting wearable devices and remote patient monitoring systems.

· Industrial IoT (IIoT): Monitoring sensor networks in manufacturing plants.

· Smart Cities: Securing traffic light systems and public infrastructure sensors.

8. Limitations and Future Work

· Limitation: The model may struggle with entirely new, unseen network architectures without retraining.

· Future Work: Implementation of Federated Learning to allow devices to learn from new threats collaboratively without sharing raw data (privacy preservation). Additionally, exploring the use of Generative Adversarial Networks (GANs) to generate synthetic attack data for retraining.