Since deep learning (DL) can automatically learn features from source code, it has been
widely used to detect source code vulnerability. To achieve scalable vulnerability scanning …

Since the Internet of Things (IoT) is widely adopted using Android applications, detecting
malicious Android apps is essential. In recent years, Android graph-based deep learning …

The function call graph (FCG) based Android malware detection methods have recently
attracted increasing attention due to their promising performance. However, these methods …

Malware is still a challenging security problem in the Android ecosystem, as malware is
often obfuscated to evade detection. In such case, semantic behavior feature extraction is …

Existing Android malware detection methods are usually hard to simultaneously resist
various obfuscation techniques. Therefore, bytecode-based code obfuscation becomes an …

Malware detection techniques achieve great success with deeper insight into the semantics
of malware. Among existing detection techniques, function call graph (FCG) based methods …

Code clone detection is to find out code fragments with similar functionalities, which has
been more and more important in software engineering. Many approaches have been …

Due to the vast array of Android applications, their multifarious functions and intricate
behavioral semantics, attackers can adopt various tactics to conceal their genuine attack …

Machine learning advances have been extensively explored for implementing large-scale
malware detection. When reported in the literature, performance evaluation of machine …

Android, the most popular mobile operating system, has attracted millions of users around
the world. Meanwhile, the number of new Android malware instances has grown …