Android Malware Detection Using Machine Learning on Image Patterns

Android platform has been targeted by cyber-criminals due to the increase number of Android users in 2017. More than 8,000 Android malware were identified everyday making it is difficult for the malware analyst to detect them. Traditional malware detection techniques are no longer reliable to detect newly created malware in short period of time. In this paper, we use a different approach to detect Android malware. The Android malware will be visualised into gray scale images and their image features will be extracted using GIST descriptor. The detection will be done and compare using three different classifiers namely k-nearest neighbor (KNN), Random Forest (RF), and Decision Tree (DT).