A Visual Analytics Framework for Explainable Malware Detection in Edge Computing Networks

The emergence of new technologies for the fifth/sixth generation (5G/6G) wireless networks has led to the development of new services, resulting in an increase in malicious activities and cyber-attacks targeting various networklayers. Edge computing, a crucial technology enabler for 6G, is expected to facilitate traffic optimisation and support new ultra- low latency services. By integrating computing power from supercomputing servers into devices at the network edge in a distributed manner, edge computing can provide consistent quality-of-service, even in remote areas, which will drive the growth of associated applications. However, the complex environment created by edge computing also poses challenges for detecting malware. Therefore, this paper proposes a novel approach to malware detection using explainability via visualization and a multi-labelling technique. An object detection algorithm is used to identify malware families within the dataset which is created by emphasizing key regions. Using features from different malware categories in an image, this model displays a thorough malware recipe. Our experiments using real malware data demonstrate that identifying malware by its visible characteristics can significantly improve the interpretability of the detection process, enhancing transparency and trustworthiness.