Classification of Mobile P2P Malware Based on Propagation Behaviour

With a multifold increase in the number of mobile users over past few years, mobile malware has emerged as a serious threat for resource constrained handheld devices. From experience of the Internet malware attacks like CodeRed and Slammer, it may not be difficult to predict the extent of devastation mobile malware could potentially cause. Numbering around 700 today, detection of mobile P2P malware may prove a serious challenge considering scarce memory, processing and battery resources of handheld devices. Issue may worsen if the detection takes place on mobile devices. Thus there is a strong need of identifying commonalities between various kinds of mobile malware to reduce the detection footprint. As a novel contribution, this work discusses various possibilities of classification of mobile malware and proposes a technical behaviour-based classification that could help detect a range of malware families in real time based on their behaviour during various stages of an attack.