A deep learning ensemble approach for malware detection in Internet of Things utilizing Explainable Artificial Intelligence

Abstract

The Internet of Things (IoT) has been popularized these days due to digitization and automation. It is deployed in various applications, i.e., smart homes, smart agriculture, smart transportation, smart healthcare, and industrial monitoring. In an IoT network, many IoT devices communicate with servers, or users access IoT devices through an open channel via a certain exchange of messages. Besides providing many benefits like efficiency, automation, and convenience, IoT presents significant security challenges due to a lack of proper standard security measures. Thus, malicious actors may be able to infect the network with malware. They may launch destructive attacks with the goal of stealing data or causing damage to the systems’ resources. This can be mitigated by introducing intrusion detection and prevention mechanisms in the network. An intelligent intrusion detection system is required to put preventative measures in place for secure communication and a malware-free network. In this article, we propose a deep learning based ensemble approach for IoT malware attack detection (in short, we call it as DLEX-IMD) trained and tested against benchmark datasets. The important measures, including accuracy, precision, recall, and F1-score, are used to evaluate the performance of the proposed DLEX-IMD. The performance of the proposed scheme is explained utilizing benchmark Explainable Artificial Intelligence (AI) method–LIME (Local Interpretable Model-Agnostic Explanations), which justifies the reliability of the proposed model training. The DLEX-IMD is also compared with a range of other closely related existing schemes and has shown better performance than those schemes with 99.96% accuracy and F1-score of 0.999.