Leveraging AI for Intrusion Detection in IoT Ecosystems: A Comprehensive Study - Shashi Bhushan Sharma; Amit Kumar Bairwa (IEEE Access)
The widespread adoption of Internet of Things (IoT) devices has brought about unprecedented connectivity and convenience, but it has also ushered in new security challenges. As IoT systems become integral parts of various domains, protecting them from malicious intrusions is crucial. This paper thoroughly examines how Artificial Intelligence (AI) techniques are applied to develop practical Intrusion Detection Systems (IDS) specifically designed for IoT environments. The examination comprehensively analyzes current state-of-the-art AI methodologies utilized in IoT-based IDS, including machine learning, deep learning, and anomaly detection algorithms. The paper delves into IoT systems’ distinctive characteristics and vulnerabilities that demand specialized intrusion detection mechanisms. It assesses various AI-based approaches’ performance, accuracy, and scalability in identifying and mitigating diverse cyber threats targeting IoT ecosystems. Beyond reviewing existing literature, the paper delves into the challenges of implementing AI-driven IDS in IoT, such as resource constraints, device heterogeneity, and dynamic network topologies. It also emphasizes potential research directions and solutions to tackle these challenges and strengthen the resilience of IoT systems against cyber threats. This all-encompassing survey aims to be a valuable resource for researchers, practitioners, and policymakers seeking to comprehend the current landscape of AI-based intrusion detection methods in IoT security. This paper contributes to the continuous efforts to fortify IoT ecosystems against evolving cybersecurity threats by shedding light on these approaches’ strengths, limitations, and prospects.