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Malware is one of the most common and severe cyber-attack today. Malwareinfects millions of devices and can perform several malicious activitiesincluding mining sensitive data, encrypting data, crippling system performance,and many more. Hence, malware detection is crucial to protect our computers andmobile devices from malware attacks. Deep learning (DL) is one of the emergingand promising technologies for detecting malware. The recent high production ofmalware variants against desktop and mobile platforms makes DL algorithmspowerful approaches for building scalable and advanced malware detection modelsas they can handle big datasets. This work explores current deep learningtechnologies for detecting malware attacks on the Windows, Linux, and Androidplatforms. Specifically, we present different categories of DL algorithms,network optimizers, and regularization methods. Different loss functions,activation functions, and frameworks for implementing DL models are presented.We also present feature extraction approaches and a review of recent DL-basedmodels for detecting malware attacks on the above platforms. Furthermore, thiswork presents major research issues on malware detection including futuredirections to further advance knowledge and research in this field.

A Survey of Recent Advances in Deep Learning Models for Detecting Malware in Desktop and Mobile Platforms