Publication Type:

Journal Article

Source:

Advances in Intelligent Systems and Computing, Springer Verlag, Volume 515, p.551-559 (2017)

ISBN:

9789811031526

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015862137&doi=10.1007%2f978-981-10-3153-3_55&partnerID=40&md5=c4b95918bd674b3882367fe1438eec14

Keywords:

Back doors, Comparative studies, Computation theory, Computer crime, Computer viruses, Computer worms, Cuckoo sandbox, Dynamic malware analysis, features extraction, Intelligent computing, Learning systems, malware, Malware analysis, Malware detection, OR-networks, Static analysis

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. © Springer Nature Singapore Pte Ltd. 2017.

Notes:

cited By 0; Conference of 5th International Conference on Frontiers in Intelligent Computing Theory and Applications, FICTA 2016 ; Conference Date: 16 September 2016 Through 17 September 2016; Conference Code:189629

Cite this Research Publication

T. G. Gregory Paul and Dr. Gireesh K. T., “A framework for dynamic malware analysis based on behavior artifacts”, Advances in Intelligent Systems and Computing, vol. 515, pp. 551-559, 2017.

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