Biblio

With the rapid development of the mobile Internet, Android has been the most popular mobile operating system. Due to the open nature of Android, c countless malicious applications are hidden in a large number of benign applications, which pose great threats to users. Most previous malware detection approaches mainly rely on features such as permissions, API calls, and opcode sequences. However, these approaches fail to capture structural semantics of applications. In this paper, we propose AMDroid that leverages function call graphs (FCGs) representing the behaviors of applications and applies graph kernels to automatically learn the structural semantics of applications from FCGs. We evaluate AMDroid on the Genome Project, and the experimental results show that AMDroid is effective to detect Android malware with 97.49% detection accuracy.

Malware authors are known to reuse existing code, this development process results in software evolution and a sequence of versions of a malware family containing functions that show a divergence from the initial version. This paper proposes the term evolved similarity to account for this gradual divergence of similarity across the version history of a malware family. While existing techniques are able to match functions in different versions of malware, these techniques work best when the version changes are relatively small. This paper introduces the concept of evolved similarity and presents automated Evolved Similarity Techniques (EST). EST differs from existing malware function similarity techniques by focusing on the identification of significantly modified functions in adjacent malware versions and may also be used to identify function similarity in malware samples that differ by several versions. The challenge in identifying evolved malware function pairs lies in identifying features that are relatively invariant across evolved code. The research in this paper makes use of the function call graph to establish these features and then demonstrates the use of these techniques using Zeus malware.

The similarity detection of the program has important significance in code reuse, plagiarism detection, intellectual property protection and information retrieval methods. Attribute counting methods cannot take into account program semantics. The method based on syntax tree or graph structure has a very high construction cost and low space efficiency. So it is difficult to solve problems in large-scale software systems. This paper uses different decision strategies for different levels, then puts forward a similarity detection method at the file level. This method can make full use of the features of the program and take into account the space-time efficiency. By using static analysis methods, we get function features and control flow features of files. And based on this, we establish the function call graph. The similar degree between two files can be measured with the two graphs. Experimental results show the method can effectively detect similar files. Finally, this paper discusses the direction of development of this method.