A Comprehensive Review of IoT Attack Detection
Taxonomy, IoT-aware Evaluation, and Research Challenges
DOI:
https://doi.org/10.14500/aro.12629Keywords:
Cyber-attack, Internet of Things, Machine learning, Review, TaxonomyAbstract
The lack of security measures in Internet of Things (IoT) systems has made these tiny devices vulnerable to increasingly advanced and evolving cyber-attacks. Attack detection and prevention are one of the most promising approaches to mitigating these attacks. However, these techniques require more computing, memory, and energy than typical IoT devices can provide. In addition, the IoT network is distributed, heterogeneous, and dynamic. These limitations motivate this review to examine the effective use of machine learning and deep learning in detecting attacks on IoT systems. Despite the presence of prior studies on reviewing these techniques, there is still a gap in analyzing attack vectors and assessing the effectiveness of current detection techniques for IoT networks and environments, especially in terms of lightweight and real-time evaluation. In this work, a multidimensional taxonomy of IoT attacks and existing detection techniques is given. The included studies were critically analyzed to evaluate and assess their effectiveness, considering performance metrics, IoT system architecture, datasets, and deployment strategies. The core methodologies of the analyzed studies were examined to guide academia and industry in improving detection techniques. Results showed that most of the proposed techniques in the literature did not address IoT-specific requirements. However, techniques featuring lightweight, real-time, federated, and scalable solutions have been proposed, yet their practical effectiveness remains unvalidated. This review addresses key research gaps and future challenges, emphasizing the need for resource-efficient, adaptable detection methods that align with IoT constraints.
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