Malware analysis is fundamental to security today. We can make use of methods and tools to detect and thus combat it. The purpose of this research area is to identify how we can study and detect malware more efficiently and more effectively across a variety of attack surfaces, including: mobile phones, desktop environments. Aspects of this research makes use of machine learning to automate the detection and analysis process.

• Jassim Happa
• Jorge Blasco Alis

• Abraham Rodríguez-Mota, Ponciano Jorge Escamilla-Ambrosio, E. Aguirre-Anaya, and Jassim Happa. Reassessing Android malware analysis: From apps to IoT system modelling. EAI Endorsed Transactions. 2018.
• Abraham Rodríguez-Mota, Ponciano Jorge Escamilla-Ambrosio, Jassim Happa, and Eleazar Aguirre-Anaya. GARMDROID: IoT potential security threats analysis through the inference of android applications hardware features requirements. In Applications for Future Internet. Springer, 2017.
• Munir Geden and Jassim Happa. Classification of malware families based on runtime behaviour. In International Symposium on Cyberspace Safety and Security. Springer. 2018.
• Christian Vaas and Jassim Happa. Detecting disguised processes using application-behavior profiling. In International Symposium on Technologies for Homeland Security (HST). IEEE. 2017.