Malware Analysis, Clustering and Classification: A Literature Review

Malware is major security threat on the Internet now-a-days. Anti-Virus companies receive large number of malware samples every day. Malware samples are classified and grouped for further analysis. There are different type of malware analysis, clustering and classification methods available. The purpose of this study is to examine the available literature on malware analysis, clustering and classification.

[1]  T. Vinay Kumar M. Tech Malwise-An Effective and Efficient Classification System for Packed and Polymorphic Malware , 2014 .

[2]  Harold Joseph Highland,et al.  A history of computer viruses - The famous 'trio' , 1997, Comput. Secur..

[3]  Zhuoqing Morley Mao,et al.  Automated Classification and Analysis of Internet Malware , 2007, RAID.

[4]  Guanhua Yan,et al.  Discriminant malware distance learning on structural information for automated malware classification , 2013, SIGMETRICS.

[5]  Ali A. Ghorbani,et al.  Automated malware classification based on network behavior , 2013, 2013 International Conference on Computing, Networking and Communications (ICNC).

[6]  L.E. Reed Performing a literature review , 1998, FIE '98. 28th Annual Frontiers in Education Conference. Moving from 'Teacher-Centered' to 'Learner-Centered' Education. Conference Proceedings (Cat. No.98CH36214).

[7]  Wanlei Zhou,et al.  Malwise—An Effective and Efficient Classification System for Packed and Polymorphic Malware , 2013, IEEE Transactions on Computers.

[8]  Robert P. Goldberg,et al.  Survey of virtual machine research , 1974, Computer.

[9]  Juan Caballero,et al.  FIRMA: Malware Clustering and Network Signature Generation with Mixed Network Behaviors , 2013, RAID.

[10]  Lynn Margaret Batten,et al.  Function length as a tool for malware classification , 2008, 2008 3rd International Conference on Malicious and Unwanted Software (MALWARE).

[11]  Kenji Kono,et al.  Distinguishing legitimate and fake/crude antivirus software , 2013, SECURWARE 2013.

[12]  Christopher Krügel,et al.  Tracking Memory Writes for Malware Classification and Code Reuse Identification , 2012, DIMVA.

[13]  Richard E. Overill,et al.  Static Analysis and Clustering of Malware Applying Text Based Search , 2013, CloudCom 2013.

[14]  Kang G. Shin,et al.  MutantX-S: Scalable Malware Clustering Based on Static Features , 2013, USENIX Annual Technical Conference.

[15]  Georg Wicherski,et al.  peHash: A Novel Approach to Fast Malware Clustering , 2009, LEET.

[16]  B. S. Manjunath,et al.  Malware images: visualization and automatic classification , 2011, VizSec '11.

[17]  Christopher Krügel,et al.  Limits of Static Analysis for Malware Detection , 2007, Twenty-Third Annual Computer Security Applications Conference (ACSAC 2007).

[18]  Christopher Krügel,et al.  A survey on automated dynamic malware-analysis techniques and tools , 2012, CSUR.

[19]  M. Masrom,et al.  Opcodes histogram for classifying metamorphic portable executables malware , 2012, 2012 International Conference on E-Learning and E-Technologies in Education (ICEEE).

[20]  Richard T. Watson,et al.  Analyzing the Past to Prepare for the Future: Writing a Literature Review , 2002, MIS Q..

[21]  Alva Erwin,et al.  Analysis of Machine learning Techniques Used in Behavior-Based Malware Detection , 2010, 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies.

[22]  Rui Yang,et al.  Detecting Malware Variants by Byte Frequency , 2011, J. Networks.

[23]  Pearl Brereton,et al.  Performing systematic literature reviews in software engineering , 2006, ICSE.

[24]  Eul Gyu Im,et al.  Malware classification method via binary content comparison , 2012, RACS.

[25]  Alexandre Gazet,et al.  Comparative analysis of various ransomware virii , 2010, Journal in Computer Virology.

[26]  M. Siddiqui,et al.  Detecting Internet Worms Using Data Mining Techniques , 2008 .

[27]  Christopher Krügel,et al.  Scalable, Behavior-Based Malware Clustering , 2009, NDSS.

[28]  Salvatore J. Stolfo,et al.  Data mining methods for detection of new malicious executables , 2001, Proceedings 2001 IEEE Symposium on Security and Privacy. S&P 2001.

[29]  Fabrice Bellard,et al.  QEMU, a Fast and Portable Dynamic Translator , 2005, USENIX ATC, FREENIX Track.

[30]  Moshe Kam,et al.  Toward an Automatic, Online Behavioral Malware Classification System , 2013, 2013 IEEE 7th International Conference on Self-Adaptive and Self-Organizing Systems.

[31]  Joris Kinable,et al.  Malware classification based on call graph clustering , 2010, Journal in Computer Virology.

[32]  Andrew Walenstein,et al.  VILO: a rapid learning nearest-neighbor classifier for malware triage , 2013, Journal of Computer Virology and Hacking Techniques.

[33]  Douglas S. Reeves,et al.  Fast malware classification by automated behavioral graph matching , 2010, CSIIRW '10.

[34]  Igor Santos,et al.  OPEM: A Static-Dynamic Approach for Machine-Learning-Based Malware Detection , 2012, CISIS/ICEUTE/SOCO Special Sessions.