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

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. (C) 2018 The Korean Institute of Communications and Information Sciences (KICS). Publishing Services by Elsevier B.V.