Transfer learning with XAI for robust malware and IoT network security.

Malware that exploits user privacy has increased in recent decades, and this trend has been linked to shifting international regulations, the expansion of Internet services, and the growth of electronic commerce. Furthermore, it is very challenging to detect privacy malware that uses obfuscation as an evasion tactic due to its behaviour, resilience, and adaptability during runtime. Forensic techniques, such as memory dumping analysis, must be used to enable a system to identify and classify patterns and behaviours that facilitate its eventual identification. This research developed a deep learning model for malware classification on an obfuscated malware dataset, called the MalwareMemoryDump dataset. It implemented transfer learning (TL) to adapt the trained model to NF-TON-IoT and UNSW-NB15, improving intrusion detection in IoT and network traffic. We conducted extensive experiments showing improved accuracy and efficiency in cross-domain detection scenarios. Further, we demonstrate that transfer learning minimises training time and computational requirements compared to training separate models from scratch. Additionally, it offers XAI-based explainability to enhance model transparency and interoperability. We demonstrated the effectiveness of the proposed model in handling diverse heterogeneous cybersecurity threats across memory-based malware analysis, IoT security, and traditional network intrusion detection. The effectiveness of the proposed methodology is evaluated using several key metrics to demonstrate its advantages over conventional methods. Experimental findings show that the proposed framework attains 99.9% accuracy on the MalwareMemoryDump dataset, 96% on the NF-Ton-IoT dataset and UNSW-NB15 datasets. Because of its innovative methodology and ability to generalise datasets, the model is a highly effective approach that outperforms many of the most recent malware detection and other security techniques.