Robust Intelligent Malware Detection Using Deep Learning

Abstract

Malicious software or malware continues to pose a major security concern in this digitalage as computer users, corporations, and governments witness an exponential growth in malware attacks.Current malware detection solutions adopt Static and Dynamic analysis of malware signatures and behaviourpatterns that are time consuming and ineffective in identifying unknown malwares. Recent malwaresuse polymorphic, metamorphic and other evasive techniques to change the malware behaviorsquicklyand to generate large number of malwares. Since new malwares are predominantly variants of existingmalwares, machine learning algorithms (MLAs) are being employed recently to conduct an effectivemalware analysis. This requires extensive feature engineering, feature learning and feature representation.By using the advanced MLAs such as deep learning, the feature engineering phase can be completelyavoided. Though some recent research studies exist in this direction, the performance of the algorithms isbiased with the training data. There is a need to mitigate bias and evaluate these methods independentlyin order to arrive at new enhanced methods for effective zero day malware detection. To fill the gapinliterature, this work evaluates classical MLAs and deep learning architectures for malware detection,classification and categorization with both public and private datasets.