Evolving trees for detecting android malware using evolutionary learning

Android smartphone platforms have grown rapidly and become ubiquitous due to the fact that they have opened up new possibilities in every aspect of modern life. However, as a consequence of their widespread, such systems suffer an increasing frequency of malware attacks. New malware or modifications to existing malware are increasingly being used by attackers in such operations. Security researchers deal with a continually evolving threat environment that is broad, complicated, and ever-changing. Conventional detection methods failed to adequately protect such systems and so the security industry has started to detect systems that use machine learning techniques are becoming more common. In this work, we used an evolutionary algorithm to identify malware targeting Android smartphones. In order to evaluate our system, we compared it to different state-of-the-art algorithms. Finally, we demonstrated our proposed method's ability to detect zero-day malware. Obtained results show that our method achieved ۹۹.۱۱\% detection accuracy, and is well-suited for detecting zero-day malware.

Keywords:

Android , smartphone , malware