On the Efficacy of Using Android Debugging Bridge for Android Device Forensics Mobile device forensics is an expanding field. As mobile devices permeate society, the ability to effectively extract forensic data from such devices is critical. Malware for mobile devices is also a growing threat. The efficacy of forensic tools is of critical importance to both researchers and practitioners. Evaluating a wide range of tools enables the selection of the appropriate tool for a given investigation. One method of evaluating forensic tools is to analyze the tools ability to detect mobile malware. Many commercial and open source forensic tools do not effectively find malware on a mobile device. This gap in capabilities requires a manual approach to extracting such data. This current study documents an experimental study to determine the efficacy of specific commercial tools in identifying malware on an Android phone. Then a methodology for utilizing the Android Debugging Bridge to identify suspect files that have a high probability of being malware. That methodology is expanded into a generalized forensic analysis approach utilizing the Android Debugging Bridge.)

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.