Evaluating Android Anti-malware against Transformation Attacks
نویسندگان
چکیده
Mobile malware threats (e.g., on Android) have recently become a real concern. In this paper, we evaluate the state-of-the-art commercial mobile anti-malware products for Android and test how resistant they are against various common obfuscation techniques (even with known malware). Such an evaluation is important for not only measuring the available defense against mobile malware threats but also proposing effective, next-generation solutions. We developed DroidChameleon, a systematic framework with various transformation techniques, and used it for our study. Our results on ten popular commercial anti-malware applications for Android are worrisome: none of these tools is resistant against common malware transformation techniques. Moreover, a majority of them can be trivially defeated by applying slight transformation over known malware with little effort for malware authors. Finally, in the light of our results, we propose possible remedies for improving the current state of malware detection on mobile devices.
منابع مشابه
ADAM: An Automatic and Extensible Platform to Stress Test Android Anti-virus Systems
With the rising threat of smartphone malware, both academic community and commercial anti-virus companies proposed many methodologies and products to defend against smartphone malware. Thus, how to assess the effectiveness of these defense mechanisms against existing and unknown malware becomes important. We propose ADAM, an automated and extensible system that can evaluate, via large-scale str...
متن کاملStealth attacks: An extended insight into the obfuscation effects on Android malware
In order to effectively evade anti-malware solutions, Android malware authors are progressively resorting to automatic obfuscation strategies. Recent works have shown, on small-scale experiments, the possibility of evading anti-malware engines by applying simple obfuscation transformations on previously detected malware samples. In this paper, we provide a large-scale experiment in which the de...
متن کاملA Large-Scale Empirical Study on the Effects of Code Obfuscations on Android Apps and Anti-Malware Products
The Android platform has been the dominant mobile platform in recent years resulting inmillions of apps and security threats against those apps. Anti-malware products aim to protect smartphone users from these threats, especially frommalicious apps. However, malware authors use code obfuscation on their apps to evade detection by anti-malware products. To assess the effects of code obfuscation ...
متن کاملYes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection
To cope with the increasing variability and sophistication of modern attacks, machine learning has been widely adopted as a statistically-sound tool for malware detection. However, its security against well-crafted attacks has not only been recently questioned, but it has been shown that machine learning exhibits inherent vulnerabilities that can be exploited to evade detection at test time. In...
متن کاملRandomizing Smartphone Malware Profiles against Statistical Mining Techniques
The growing use of smartphones opens up new opportunities for malware activities such as eavesdropping on phone calls, reading email and call-logs, and tracking callers’ locations. Statistical data mining techniques have been shown to be applicable to detect smartphone malware. In this paper, we demonstrate that statistical mining techniques are prone to attacks that lead to random smartphone m...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2013