DESIGNING A SIMPLE INTRUSION DETECTION SYSTEM FOR SMART HOME IOT DEVICE

The proliferation of Internet of Things (IoT) devices in smart home environments has significantly expanded the cyberattack surface, exposing users to threats such as Distributed Denial-of-Service (DDoS), brute-force attacks, and reconnaissance scans. Traditional security mechanisms including firewalls and antivirus software are often inadequate due to the resource constraints and heterogeneity of IoT devices. This study evaluates the effectiveness of Snort, an open-source, rule-based Network Intrusion Detection System (NIDS), in detecting IoT-specific cyber threats within a simulated smart home network. The system was implemented using emulated IoT devices (e.g., cameras, smart locks, sensors) and tested against common attack vectors. Performance metrics including detection accuracy (95%), false positive rate (3%), CPU usage (45%), and memory consumption (512 MB) demonstrate Snort’s viability as a lightweight, real-time defense mechanism. While highly effective for known threats, Snort’s signature-based approach shows limitations against zero-day and polymorphic attacks. The study concludes with recommendations for augmenting Snort with anomaly detection and machine learning to enhance adaptability, and underscores the importance of regular rule updates and multi-layered security strategies in IoT deployments.