A malware detector is a system that attempts to determine whether a program has malicious intent. In order to evade detection, malware writers (hackers) frequently use obfuscation to morph malware. Malware detectors that use a pattern matching approach (such as commercial virus scanners

. From this perspective, we propose and analyze some potential limitation-oriented techniques for effective malware detection and prevention on mobile phones. Keywords-Mobile Malware; Malware Detection; android malware; I.

The application of machine learning methods to malware detection has opened up possibilities of generating large number of classifiers that use different kinds of features and learning algorithms. A straightforward way to select the best classifier is to pick the one with best holdout or cross

Stealthy malware, such as botnets and spyware, are hard to detect because their activities are subtle and do not disrupt the network, in contrast to DoS attacks and aggressive worms. Stealthy malware, however, does communicate to exfiltrate data to the attacker, to receive the attacker’s commands

, botnets, and worms. Malicious programs spy on users ’ behavior and compromise their privacy. Unfortunately, existing techniques for detecting malware and analyzing unknown code samples are insufficient and have significant shortcomings. We observe that malicious information access and processing behavior

technique will be introduced to detect malware. This technique detects malware in android applications through machine learning classifier by using both static and dynamic analysis. This technique does not rely on malware signatures for static analysis but instead android permission model is used. Under

Abstract-The internet plays an important role in all areas of society from economy to the government. Nowadays Computer Security is pretentious by malicious data. Computer security means keeping the information on computer in a secure manner. so detection of malware is an paramount substance

Dramatic increase in smartphone sales and third-party applications that users can download has significantly increased the possibility of rootkits and malware targeted for smartphones. This paper discusses the current state of research in detection and mitigation of propagation of malicious code

Polymorphic and metamorphic malware use code obfuscation techniques to construct new variants which preserve the semantics of the original but change the code syntax, evading current compiled code based detection methods. Dynamic slicing is a technique that, given a variable of interest within a

I would like to thank my advisor Dr. Thomas Stibor for fruitful discussion and helpful advice. Moreover I would like to thank my family for continuous support. Within the thesis, an environment for gathering behavior data of Windows XP processes is developed. The behavior data consists 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 that is automatically generated by a parser reading the Microsoft Windows SDK. The parser was specially built for this process and is based on the Microsoft C language grammar. The Windows SDK contains some extensions to the C language to make interface annotations. These interface annotations describe each parameter of a function in detail. The code generator is using all this information to prepare the gathering of function traces.