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Paper Title : Multilayer Perception Classifier for Malware Detection using Machine Learning Algorithm

ISSN : 2395-1303
Year of Publication : 2022
10.5281/zenodo.7115219

Authors: Dr D.J. Samatha Naidu, P. Balaji

         


Abstract
Mobile phones are a significant component of people's life and are progressively engaged in these technologies. Increasing customer numbers encourages the hackers to make malware. In addition, the security of sensitive data is regarded lightly on mobile devices. Based on current approaches, recent malware changes fast and thus become more difficult to detect. An alternative solution to detect malware using anomaly-based classifier is proposed.

Reference
[1]. Sanjay Chakrabortya and Lopamudra Dey. A rule-based probabilistic technique for malware code detection. Multiagent and Grid Systems – An International Journal, IOS Press, 12, 2016, pp. 271–286 271. DOI 10.3233/MGS-160254 [2]. Y. Zhou, Z. Wang, W. Zhou, and X. Jiang. Hey, you, get off of my market: Detecting malicious apps in official and alternative android markets. in NDSS, vol. 25, no. 4, 2012, pp. 50–52. [3]. D. Keragala. Detecting malware and sandbox evasion techniques, SANS Institute InfoSec Reading Room, 2016. URL: https://www.sans.org/reading-room/whitepapers/ forensics/detecting-malware-sandbox-evasion-techniques-36667. [4]. Sharif, M., Yegneswaran, V., Saidi, H., Porras, P., and Lee, W. Eureka: A framework for enabling static malware analysis. In Computer security-ESORICS 2008, pages 481- 500. Springer. [5]. Moser, A., Kruegel, C., and Kirda, E. Limits of static analysis for malware detection. In Computer security applications conference, ACSAC 2007. Twenty-third annual, 2007, pages 421-430.
Keywords
— detection malware, cybersecurity, machine learning, Multilayer perception, classifier, analysis, memory forensics.