Abstract
Malware stands for malicious software. Any file that causes damage to the computer or network can be termed as malicious. For malware analysis, there are two fundamental approaches: static analysis and dynamic analysis. The static analysis focuses on analyzing the file without executing, whereas dynamic analysis means analyzing or observing its behavior while it is being executed. While performing malware analysis, we have to classify malware samples. The different types of malware include worm, virus, rootkit, trojan horse, back door, botnet, ransomware, spyware, adware, and logic bombs. In this paper, our objective is to have a breakdown of techniques used for malware analysis and a comparative study of various malware detection/classification systems.
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References
Sikorski, Michael, and Andrew Honig. Practical Malware Analysis: The Hands-On Guide to Dissecting Malicious Software. No Starch Press, 2012.
Egele, Manuel, et al. “A survey on automated dynamic malware-analysis techniques and tools.” ACM Computing Surveys (CSUR) 44.2 (2012): 6.
Mohaisen, Aziz, Omar Alrawi, and Manar Mohaisen. “Amal: High-fidelity, behavior-based automated malware analysis and classification.” Computers & Security (2015).
Malware tips, https://malwaretips.com.
Pirscoveanu, Radu S., et al. “Analysis of Malware behavior: Type classification using machine learning.” Cyber Situational Awareness, Data Analytics and Assessment (CyberSA), 2015 International Conference on. IEEE, 2015.
Shijo, P. V., and A. Salim. “Integrated Static and Dynamic Analysis for Malware Detection.” Procedia Computer Science 46 (2015): 804–811.
Naval, Smita, et al. “Employing Program Semantics for Malware Detection.” Information Forensics and Security, IEEE Transactions on 10.12 (2015): 2591–2604.
University of Waikato, http://www.cs.waikato.ac.nz.
Kawaguchi, Naoto, and Kazumasa Omote. “Malware Function Classification Using APIs in Initial Behavior.” Information Security (AsiaJCIS), 2015 10th Asia Joint Conference on. IEEE, 2015.
Ozsoy, Meltem, et al. “Malware-aware processors: A framework for efficient online malware detection.” High Performance Computer Architecture (HPCA), 2015 IEEE 21st International Symposium on. IEEE, 2015.
Cuckoo Sandbox, http://www.cuckoosandbox.org.
Jiang, Xuxian, Xinyuan Wang, and Dongyan Xu. “Stealthy malware detection through vmm-based out-of-the-box semantic view reconstruction.” Proceedings of the 14th ACM conference on Computer and communications security. ACM, 2007.
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Gregory Paul, T.G., Gireesh Kumar, T. (2017). A Framework for Dynamic Malware Analysis Based on Behavior Artifacts. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-10-3153-3_55
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DOI: https://doi.org/10.1007/978-981-10-3153-3_55
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