, features of known-benign and known-malicious applications are extracted for machine learning to provide baseline behavior datasets. Test applications are then passed through the behavior based module for identification of its being malware or benign. A classification scheme is provided for applications

of the threat posed by malware and of actual in-fections on their devices. In this paper we explore techniques to enable mobile network operators to detect Android malware and violations of user pri-vacy through network traffic analysis. 1

Abstract. In the most comprehensive study on Android attacks so far

anal-ysis for deciding if a given application matches a malware signature. The signature matching algorithm of Apposcopy uses a combination of static taint analysis and a new form of program representation called Inter-Component Call Graph to efficiently detect Android applications that have certain

Abstract—The design of the Android system allows applica-tions to load additional code from external sources at runtime. On the one hand, malware can use this capability to add malicious functionality after it has been inspected by an application store or anti-virus engine at installation time

and detect malware on this platform. Users are generally naïve and are fooled into downloading apps, which may be harmful or malicious. They do not have enough experience with either the platform or the apps to know what can be malicious and what is genuine. Thus, the goal of the authors is to create a

, features of known-benign and known-malicious applications are extracted for machine learning to provide baseline behavior datasets. Test applications are then passed through the behavior based module for identification of its being malware or benign. A classification scheme is provided for applications

of parameterized system calls to the Win32 API of benign and malicious processes. The collection of the behavior data involves both specially designed user and kernel mode modules in a virtualized Windows XP system as well as an external java program controlling the experiments. The data is gathered using code

Abstract—With the advent of mobile era, mobile terminals are going through a trend of surpassing PC to become the most popular computing device. Meanwhile, the hackers and virus-writers are paying close attention to the mobile terminals, es-pecially the Android platform. The growing of malwares

The Internet has witnessed a tremendous growth the last years. Undoubtedly, its services and mostly the World Wide Web have become an integral part in the lives of hundreds of millions of people, who use it in daily basis. Unfortunately, as the Internet’s popularity increases, so does the interest of attackers who seek to exploit vulnerabilities in users ’ machines. What was primary meant to be mainly a competition among computer experts to test and improve their technical skills has turned into a multi-billion dollar business. Nowadays, attackers try to take under their control vulnerable computers that will allow them to perform their nefarious tasks, such as sending spam emails, launching distributed denial-of-service (DDoS) attacks, generate revenue from online advertisements by performing click-frauds, or stealing personal data like email accounts and banking credentials. In this dissertation, we address the security issues online users face every day from two points of view. First, we investigate how infected computers that constitu-te a botnet—network of compromised machines which are remotely controlled by