Authors - Maryam Ghazi Ali, Bindu V. R Abstract - The Internet of Things (IoT) has spread rapidly, significantly increasing several secu-rity vulnerabilities, as traditional detection systems are becoming insufficient to manage the vol-ume and diversity of traffic that characterizes modern networks. The review provides a compre-hensive analysis of recent advances in learning-based intrusion detection systems (IDS), focusing primarily on deep learning, traditional learning, machine learning, and hybrid frameworks. Through critically evaluating a diverse range of state-of-the-art studies, the review explores dif-ferent methodological solutions, data, and performance measurement in the field. The available empirical results show that, although deep learning models are better at identifying complex pat-terns in the data, traditional machine learning algorithms require less computational power. In addition, hybrid and ensemble models often outperform single-method options, but often with high computational cost. The review outlines a number of important challenges, including the issue of class imbalance and the fact that models are not very interpretable. It argues that light-weight and interpretable AI systems should be a priority in future studies, and the gap between theoretical academic frameworks and practical IoT applications would be minimized.
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