Incident Response and Forensics in Compromised IoT Systems

Abstract

The rapid proliferation of Internet of Things (IoT) devices has fundamentally transformed the digital landscape, introducing unprecedented connectivity and automa tion across industrial, commercial, and consumer sectors. However, this expansion has concurrently broadened the attack surface, rendering IoT networks highly susceptible to sophisticated cyber threats. This chapter presents a comprehensive examination of in cident response and digital forensics methodologies specifically tailored for compromised IoT systems. A hybrid machine learning-based framework for anomaly detection and au tomated incident triage is proposed and evaluated using the CIC IoT 2023 dataset. The research methodology integrates network traffic analysis, forensic evidence collection, and timeline reconstruction to facilitate rapid containment and eradication of threats. Sim ulation results demonstrate that the proposed hybrid model achieves a 97.6% detection accuracy, significantly outperforming traditional classification algorithms. Furthermore, automated response orchestration reduces containment time by 83% compared to man ual processes. This chapter addresses the unique challenges of IoT forensics, including resource constraints, heterogeneous architectures, and volatile data preservation, offering scalable solutions for securing modern IoT infrastructures.