Intrusion Detection in Internet of Things With MQTT Protocol-An Accurate and Interpretable Genetic-Fuzzy Rule-Based Solution

This article 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 multiobjective evolutionary optimization algorithm to optimize the accuracy-interpretability tradeoff 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 an 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.

Automated summary

This article proposes an accurate and interpretable intrusion detection method for IoT systems using a knowledge-discovery data-mining/machine-learning approach. The approach, implemented as a fuzzy rule-based classifier, optimizes the tradeoff between accuracy and interpretability of IoT intrusion detection systems (IoT IDSs) through a multiobjective evolutionary optimization algorithm. The main contribution of this work is the design of accurate and interpretable IoT IDSs based on recently published MQTT-IOT-IDS2020 data sets, which describe the behavior of an MQTT-protocol-based IoT system. A comparison with seven alternative approaches demonstrates that the proposed method significantly outperforms others in terms of interpretability while remaining competitive or superior in accuracy.