Nataraj, L., Yegneswaran, V., Porras, P. and Zhang, J. (2011) A Comparative Assessment of Malware Classification Using Binary Texture Analysis and Dynamic Analysis. Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, 21-30.

has been cited by the following article:

• TITLE: Malware Analysis and Classification: A Survey

  AUTHORS: Ekta Gandotra, Divya Bansal, Sanjeev Sofat

  KEYWORDS: Malware; Static Analysis; Dynamic Analysis; Machine Learning; Classification; Clustering

  JOURNAL NAME: Journal of Information Security, Vol.5 No.2, April 1, 2014

  ABSTRACT: One of the major and serious threats on the Internet today is malicious software, often referred to as a malware. The malwares being designed by attackers are polymorphic and metamorphic which have the ability to change their code as they propagate. Moreover, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses which typically use signature based techniques and are unable to detect the previously unknown malicious executables. The variants of malware families share typical behavioral patterns reflecting their origin and purpose. The behavioral patterns obtained either statically or dynamically can be exploited to detect and classify unknown malwares into their known families using machine learning techniques. This survey paper provides an overview of techniques for analyzing and classifying the malwares.