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Research shows that over the last decade, malware has been growingexponentially, causing substantial financial losses to various organizations.Different anti-malware companies have been proposing solutions to defendattacks from these malware. The velocity, volume, and the complexity of malwareare posing new challenges to the anti-malware community. Currentstate-of-the-art research shows that recently, researchers and anti-virusorganizations started applying machine learning and deep learning methods formalware analysis and detection. We have used opcode frequency as a featurevector and applied unsupervised learning in addition to supervised learning formalware classification. The focus of this tutorial is to present our work ondetecting malware with 1) various machine learning algorithms and 2) deeplearning models. Our results show that the Random Forest outperforms DeepNeural Network with opcode frequency as a feature. Also in feature reduction,Deep Auto-Encoders are overkill for the dataset, and elementary function likeVariance Threshold perform better than others. In addition to the proposedmethodologies, we will also discuss the additional issues and the uniquechallenges in the domain, open research problems, limitations, and futuredirections.

lecture notes in computer science

Malware Detection using Machine Learning and Deep Learning

pdf-texts/10.1007_978-3-030-04780-1_28.txt