Volume 10 , Issue 1 , PP: 34-42, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Marwa M. Eid 1 * , M. I. Fath Allah 2
Doi: https://doi.org/10.54216/JCIM.100102
Malware is software that is designed to cause damage to computer systems. Locating malicious software is a crucial task in the cybersecurity industry. Malware authors and security experts are locked in a never-ending conflict. In order to combat modern malware, which often exhibits polymorphic behavior and a wide range of characteristics, novel countermeasures have had to be created. Here, we present a hybrid learning approach to malware detection and classification. In this scenario, we have merged the machine learning techniques of Random Forest and K-Nearest Neighbor Classifier to develop a hybrid learning model. We used current malware and an updated dataset of 10,000 examples of malicious and benign files, with 78 feature values and 6 different malware classes to deal with. We compared the model's results with those of current approaches after training it for both binary and multi-class classification. The suggested methodology may be utilized to create an anti-malware application that is capable of detecting malware on newly collected data.
Cybersecurity , Malware detection , Machine learning , Hybrid learning , Classification , K-Nearest neighbor , Random forest , Metaheuristic optimization
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