A Review of the Progressive Odyssey of AI-Driven Intrusion Detection Within Embedded Systems

Security of Embedded Systems (ES) has become a major concern due to their growing usage in numerous industries. Their connectivity to the internet made them vulnerable to sophisticated cyber-attacks. One of the most important strategies for strengthening their security posture is using Intrusion Detection Systems (IDS). However, the limited resources of ES make it difficult to utilize IDS. This paper reviews the primary studies that contributed to developing IDS systems applicable to ES. It examines the challenges of building such systems, reports the current trends, and proposes future recommendations to enhance the deployment of IDS in ES. The findings showed that most studies currently employ machine and deep learning algorithms to build IDS for ES. Although significant results were achieved, several gaps were reported. The proposed frameworks did not investigate the security, privacy, and interpretability concerns of employing machine and deep learning. Moreover, a feasible framework to address all the ES resource constraints is lacking. Future recommendations include solutions to enhance such models’ security, privacy, and interoperability. Moreover, it includes the employment of differential privacy, explainable artificial intelligence, federated learning, and trusted executed environments.