Intelligent Malware Detection System

Abstract

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 is the fundamental trait of numerous malware categories breaching users’ privacy (including key loggers, password thieves, network sniffers, stealth backdoors, spyware and root kits), which separates these malicious applications from benign software. Commercial anti-virus software is unable to provide protection against newly launched (“zero-day”) malware. In this dissertation work, we propose a novel malware detection technique which is based on the analysis of byte-level file content. The proposed dissertation work will demonstrate the implementation of system for detection of various types of malware.