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Abstract:

This paper addresses the problem of an accurate and interpretable intrusion detection in Internet-of-Things (IoT) systems using the knowledge-discovery data-mining/machine-learning approach proposed by us. This approach – implemented as a fuzzy rule-based classifier – employs our generalization of the well-known multi-objective evolutionary optimization algorithm to optimize the accuracy-interpretability trade-off of the IoT intrusion detection systems (IoT IDSs). The main contribution of this work is the design of accurate and interpretable IoT IDSs from the most recently published data – referred to as MQTT-IOT-IDS2020 data sets – describing the behavior of a MQTT-protocol-based IoT system. A comparison with seven available alternative approaches was also performed demonstrating that the approach proposed by us significantly outperforms alternative methods in terms of interpretability of intrusion-detection decisions made while remaining competitive or superior in terms of the accuracy of those decisions.

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Intrusion Detection in Internet of Things with MQTT Protocol – an Accurate and Interpretable Genetic-Fuzzy Rule-Based Solution