Advances In Adaptive Machine Learning Algorithms for Enhanced Security In IoT Networks

The accelerated growth and diversification of Internet of Things (IoT) environments have compounded security issues that are caused by non-stationary traffic, dynamic attacker tactics, and extreme resource constraints. In this case, the failure of the existing process for preventing intrusion through conventional, manually configured intrusion detection systems is becoming less sustainable, while adaptive machine learning (ML)-based security systems are becoming more popular. Nonetheless, the survey literature is generally more biased toward algorithmic enumeration or single-detection accuracy and provides very little critical evaluation of adaptation mechanisms, dataset realism, real-time capability, and implementation constraints. This paper provides a systematic, deployment-conscious review of adaptive machine learning methods for IoT security, conducted through a PRISMA-directed, systematic literature review. An integrative taxonomy of learning paradigms and adaptation methods is presented, a critical review of popular datasets on IoT security is conducted, and a discussion of performance metrics beyond accuracy, such as false positives, robustness, latency, and energy overhead, is provided. The review also examines real-time deployment issues related to edge-cloud, resource constraints, retraining costs, and orchestration complexity, as well as the security of adaptive models against adversarial manipulation and privacy leakage. The literature review, by reconceptualizing the problem of IoT intrusion detection as an adversarially exposed, adaptive learning task with deployment constraints, identifies gaps in the field and provides directions for future work to build a scalable, reliable, and dependable IoT security system.