IDSIoTAI: An AI-Driven Intrusion Detection Framework for Smart-Home IoT Environments using Naïve Bayes and Support Vector Machine algorithms

Abstract

The explosion of Internet of Things (IoT) deployment over the past decade has served as a foundational pillar for global digital transformation. However, the rapid expanding attack surface of IoT architectures often suffers from compromised security paradigms, rendering smart environments highly vulnerable to malicious exploitations. While traditional Intrusion Detection Systems (IDS) mitigate network threats, conventional datasets lack the granular, protocol-specific traffic anomalies characteristic of IoT environments. This research addresses this gap by developing an automated machine learning framework designed to differentiate reconnaissance and anomalous activities from baseline behaviors within smart home IoT infrastructures. Utilizing the Hacking and Countermeasure Research Lab (HCRL) dataset, we evaluate and contrast the efficacy of Naïve Bayes (NB) and Support Vector Machine (SVM) algorithms across varying data-split ratios. Experimental results indicate that while Naïve Bayes offers competitive computational recall in localized environments, the SVM classifier demonstrates superior robustness, achieving an accuracy threshold approaching 99.99% in isolating low-frequency reconnaissance attacks.