Published Paper Details:

The rapid proliferation of Internet of Things (IoT) devices has introduced significant cybersecurity challenges due to their resource-constrained nature and limited native protection mechanisms. Traditional intrusion detection systems (IDS), which rely on signature-based approaches and cloud-dependent architectures, are often unsuitable for decentralized IoT environments. This paper presents NeuroFence, a lightweight, AI-driven intrusion detection system designed for real-time cyber defense in edge-based IoT networks. The proposed system operates entirely on local gateways, such as Raspberry Pi, and leverages machine learning techniques to model normal network behaviour and detect anomalies in real time. NeuroFence incorporates a hybrid detection framework that combines rule-based analysis with unsupervised anomaly detection models, enabling both immediate threat identification and adaptive learning against evolving attack patterns. The system captures live network traffic, extracts features using sliding-window techniques, and evaluates traffic behaviour using models such as Isolation Forest and TinyML-based classifiers. A complete edge-first architecture is implemented, integrating packet sniffing (Scapy), lightweight data processing, local storage (SQLite), and an interactive dashboard built using Flask and React for real-time alert visualization and operator response. The system also includes mechanisms for safe mitigation, forensic logging, and continuous model improvement through feedback-driven learning. Experimental evaluation demonstrates that NeuroFence effectively detects anomalous IoT traffic with low latency and minimal computational overhead, making it suitable for deployment in resource-constrained environments such as smart homes, industrial IoT systems, and remote installations. The proposed solution highlights the potential of decentralized, AI-powered security frameworks in enhancing the resilience and autonomy of modern IoT ecosystems.