Analyzing and Detecting Malicious Activities in Emerging Communication Platforms

Benefiting from innovatory techniques, two communication platforms (online social networking (OSN) platforms and smartphone platforms) have emerged and been widely used in the last few years. However, cybercriminals have also utilized these two emerging platforms to launch malicious activities such as sending spam, spreading malware, hosting botnet command and control (C&C) channels, and performing other illicit activities. All these malicious activities may cause significant economic loss to our society and even threaten national security. Thus, great efforts are indeed needed to mitigate malicious activities on these advanced communication platforms.

The goal of this research is to make a deep analysis of malicious activities on OSN and smartphone platforms, and to develop effective and efficient defense approaches against those malicious activities. Firstly, this dissertation performs an empirical analysis of the cyber criminal ecosystem on a large-scale online social networking website space. Secondly, through reverse engineering OSN spammers? tastes (their preferred targets to spam), this dissertation provides guidelines for building more effective social honeypots on the online social networking platforms, and generates new insights to defend against OSN spammers. Thirdly, this dissertation shows a comprehensive empirical study on analyzing the market-level and network-level behaviors of the Android malware ecosystem. Lastly, by grouping the common program logic among malware families, this dissertation designs an effective system to automatically detect Android malware.