Android Malware Classification Using XGBoost On Data Image Pattern The popularity of Android smartphone has encouraged cybercriminals to develop malware targeting this platform. In the third quarter of 2018, the number of Android malware has increased by 40% when compared with the same quarter of year 2017. The traditional malware analysis techniques need to be improved where it must be able to detect new malware quickly and accurately. This paper proposed an Android malware classification by using 8bit grayscale images where the images features will be extracted using GIST descriptor and later be classified using XGBoost machine learning algorithm. Classes.dex and its data section will be extracted from the Android APK files before they were converted into images. kNearest Neighbours and Random Forest machine learning algorithms will also be used to compare the accuracy performance. The experiments show classification using data section performed better than classification on full classes.dex files in all three machine learning algorithms.

A Robust Malware Detection Approach for Android System Against Adversarial Example Attacks In recent years, Android has become the leading smartphone operating system across the world. However, due to their increasing popularity, Android devices have become the primary target to mobile malware. To address the arising security threats, many malware detection approaches have been studied that aim at providing strong defense mechanisms against malware. However, with more such malware detection systems being distributed and deployed, malware authors tend to generate adversarial examples by manipulating mobile applications to avoid being detected by the malware detection systems. In this paper, we investigate different types of adversarial example attacks while researching a viable approach to fight against them. More specifically, we first present the literature review on both existing malware detection approaches and adversarial example attacks against them. Then, we focus on the widely used evasion attack model that is applied to generate mutated samples. By working with various app features such as binary Ngrams of API calls, we will generate feature sets consisting of a selected range of binary Ngrams. As a result, we intend to use the manipulated dataset to develop and train our classifier to detect the evasion attack, and the goal of our approach is to further enhance the robustness of malware detection approach in the presence of adversarial example attacks.