Using heuristic approach to build Anti-malware
Muath Alrammal, Munir Naveed, Samer Rihawi · 2018
The security threats to mobile devices are growing exponentially as the degree of sophistication for these smart devices increase. Mobile devices are not only used for phone calls, they process several enterprise and personal information on daily basis. The comprise to such crucial and private information can cause devastating effects at personal and enterprise levels. In this work, we present a new approach to develop anti-malware programs that can adapt to themselves to the new definitions of the malwares and can detect them in realtime. We propose a machine learning algorithm that can be trained to identify a malicious activity on a smart-phone. We develop a malware and also used existing malwares to generate a training data set to train a malware for detecting the patterns of malicious activities on devices. The paper also presents the key indicators for pattern matching in such cases. The results show that the new definitions of malicious codes are not detectable by existing sophisticated anti-malware programs by using sandboxing techniques. The results also show that all definitions of malwares work under within the same patterns of indicators.