Abstract: The recent increase in the number of Internet of Things (IoT) devices has vastly increased the cyber-attack surface of the latest networks. Even though intrusion detection using machine learning (ML) has demonstrated encouraging results in terms of detection accuracy, most of the current methodologies are based on computationally intensive or black box models that are not transparent and do not fit the resource-limited IoT worlds. Furthermore, the lack of explanation reduces trust and makes it difficult to apply in the real world. This paper offers an effective explainable ML model for reliable intrusion detection to resolve all these challenges that are specific to the resource-constrained IoT setting. Our proposed framework combines lightweight feature engineering, an effective machine learning-driven detection model, and a layer of Explainable Artificial Intelligence (XAI), offering an insight into the feature-based part of intrusion detection decisions. The framework allows improvement in transparency, trust, and usability by combining detection accuracy and interpretability. The efficiency of the suggested system has been assessed using one of the popular standard datasets, NSL-KDD, allowing consideration of performance in both traditional network traffic and real IoT attacks. The experimental findings indicate that the suggested framework can be used to achieve competitive intrusion detection performance, enhance interpretability, and lower the computation overhead significantly. These findings affirm that reliable intrusion detection can be performed effectively and interpretably without compromising efficiency, and that the proposed framework is viable for real-time implementation on resource-limited IoT systems.

Key words: cybersecurity, explainable artificial intelligence, internet of things, machine learning, resource-constrained environment