Detecting Repackaged Android Applications Using Perceptual Hashing

The last decade has shown a steady rate of Android device dominance in market share and the emergence of hundreds of thousands of apps available to the public. Because of the ease of reverse engineering Android applications, repackaged malicious apps that clone existing code have become a severe problem in the marketplace. This research proposes a novel repackaged detection system based on perceptual hashes of vetted Android apps and their associated dynamic user interface (UI) behavior. Results show that an average hash approach produces 88% accuracy (indicating low false negative and false positive rates) in a sample set of 4878 Android apps, including 2151 repackaged apps. The approach is the first dynamic method proposed in the research community using image-based hashing techniques with reasonable performance to other known dynamic approaches and the possibility for practical implementation at scale for new applications entering the Android market

Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild

In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild. In particular, we focus on four popular obfuscation approaches: identifier renaming, string encryption, Java reflection, and packing. To obtain the meaningful statistical results, we designed efficient and lightweight detection models for each obfuscation technique and applied them to our massive APK datasets (collected from Google Play, multiple third-party markets, and malware databases). We have learned several interesting facts from the result. For example, malware authors use string encryption more frequently, and more apps on third-party markets than Google Play are packed. We are also interested in the explanation of each finding. Therefore we carry out in-depth code analysis on some Android apps after sampling. We believe our study will help developers select the most suitable obfuscation approach, and in the meantime help researchers improve code analysis systems in the right direction

Evaluation of Android anti-malware resistance against transformation attacks

Android being most popular and user-friendly is targeted by most of the malware authors. The malware authors use various transformation techniques to create different variants of malwares. Different transformation techniques such as obfuscation, repackaging, renaming are used mostly. Many anti-malwares are developed to secure the Android devices. Android does not offer file access permissions to all the applications installed. Thus anti-malwares may not provide complete security to the Android devices. In this paper, many such different techniques are presented that can be used to evaluate different anti-malwares

A Survey of Evaluation Techniques for Android Anti-Malware using Transformation Attacks

Android an open-source operating system mainly used for mobile phones have become increasingly popular. Studies suggest that mobile malware threats have recently become a real concern and the impact of malware is getting worse. 2014 saw an astounding 75 percent increase in the Android mobile malware. It is therefore imperative to evaluate the resistance and robustness of anti-malware products for android against various malware. To evaluate existing anti-malware, a systematic framework called DroidChameleon is developed with several common transformation techniques. This survey examines the effectiveness and robustness of popular antimalware tools and compare them against one another aiding in the decision making process involved with developing a secure system

Malware Analysis and Privacy Policy Enforcement Techniques for Android Applications

The rapid increase in mobile malware and deployment of over-privileged applications over the years has been of great concern to the security community. Encroaching on user’s privacy, mobile applications (apps) increasingly exploit various sensitive data on mobile devices. The information gathered by these applications is sufficient to uniquely and accurately profile users and can cause tremendous personal and financial damage. On Android specifically, the security and privacy holes in the operating system and framework code has created a whole new dynamic for malware and privacy exploitation. This research work seeks to develop novel analysis techniques that monitor Android applications for possible unwanted behaviors and then suggest various ways to deal with the privacy leaks associated with them. Current state-of-the-art static malware analysis techniques on Android-focused mainly on detecting known variants without factoring any kind of software obfuscation. The dynamic analysis systems, on the other hand, are heavily dependent on extending the Android OS and/or runtime virtual machine. These methodologies often tied the system to a single Android version and/or kernel making it very difficult to port to a new device. In privacy, accesses to the database system’s objects are not controlled by any security check beyond overly-broad read/write permissions. This flawed model exposes the database contents to abuse by privacy-agnostic apps and malware. This research addresses the problems above in three ways. First, we developed a novel static analysis technique that fingerprints known malware based on three-level similarity matching. It scores similarity as a function of normalized opcode sequences found in sensitive functional modules and application permission requests. Our system has an improved detection ratio over current research tools and top COTS anti-virus products while maintaining a high level of resiliency to both simple and complex obfuscation. Next, we augment the signature-related weaknesses of our static classifier with a hybrid analysis system which incorporates bytecode instrumentation and dynamic runtime monitoring to examine unknown malware samples. Using the concept of Aspect-oriented programming, this technique involves recompiling security checking code into an unknown binary for data flow analysis, resource abuse tracing, and analytics of other suspicious behaviors. Our system logs all the intercepted activities dynamically at runtime without the need for building custom kernels. Finally, we designed a user-level privacy policy enforcement system that gives users more control over their personal data saved in the SQLite database. Using bytecode weaving for query re-writing and enforcing access control, our system forces new policies at the schema, column, and entity levels of databases without rooting or voiding device warranty