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In recent years, as mobile smart device sales grow quickly, the development of mobile applications (apps) keeps accelerating, so does mobile app repackaging. Attackers can easily repackage an app under their own names or embed advertisements to earn pecuniary profits. They can also modify a popular app by inserting malicious payloads into the original app(More)
In this work, we address the problem of algorithm plagiarism, which occurs when a plagiarist, violating intellectual property rights, steals others' algorithms and covertly implements them. In contrast to software plagiarism, which has been extensively studied, limited attention has been paid to algorithm plagiarism. In this paper, we propose two dynamic(More)
JavaScript based attacks have been reported as the top Internet security threats in recent years. Since most of the Internet users rely on anti-virus software to protect themselves from malicious JavaScript code, attackers exploit JavaScript obfuscation techniques to evade the detection of anti-virus software. To better understand the obfuscation techniques(More)
As one of the most popular mobile platforms, the Android system implements an install-time permission mechanism to provide users with an opportunity to deny potential risky permissions requested by an application. In order for both users and application vendors to make informed decisions, we designed and built Permlyzer, a general-purpose framework to(More)
The dynamic features of the JavaScript language not only promote various means for users to interact with websites through Web browsers, but also pose serious security threats to both users and websites. On top of this, obfuscation has become a popular technique among malicious JavaScript code that tries to hide its malicious purpose and to evade the(More)
Software plagiarism, an act of illegally copying others' code, has become a serious concern for honest software companies and the open source community. In this paper, we propose LoPD, a program logic based approach to software plagiarism detection. Instead of directly comparing the similarity between two programs, LoPD searches for any dissimilarity(More)
Microarray data are high dimension with high noise ratio and relatively small sample size, which makes it a challenge to use microarray data to identify candidate disease genes. Here, we have presented a hybrid method that combines estimation of distribution algorithm with support vector machine for selection of key feature genes. We have benchmarked the(More)