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DeepMalNet: Evaluating shallow and deep networks for static PE malware detection

Publication Type : Journal Article

Publisher : ICT Express

Source : ICT Express, Korean Institute of Communications Information Sciences (2018)

Url :

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Computer Science, Electronics and Communication

Year : 2018

Abstract : This paper primarily evaluates the efficacy of shallow and deep networks to statically detect malicious windows portable executable (PE) files. This uses recently released, labeled and benchmark data set, EMBER malware benchmark data set. As deep networks are parameterized, the parameters are chosen based on comparing the performance of various network parameters and network topologies over various trials of experiments. The experiments of such chosen efficient configurations of deep models are run up to 1000 epochs with varying learning rates between 0.01 and 0.5. The observed results of deep networks are high compared to the shallow networks. © 2018 The Korean Institute of Communications and Information Sciences (KICS)

Cite this Research Publication : V. R. and Dr. Soman K. P., “DeepMalNet: Evaluating shallow and deep networks for static PE malware detection”, ICT Express, 2018.

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