Deep learning models for multi-class malware classification using Windows exe API calls Online publication date: Mon, 07-Mar-2022
by Kakelli Anil Kumar; Kaustubh Kumar; Nag Lohith Chiluka
International Journal of Critical Computer-Based Systems (IJCCBS), Vol. 10, No. 3, 2022
Abstract: Metamorphic malware is new and one of the most advanced malwares recently discovered. This malware can bypass anti-viruses and are much harder to detect if present in any computer system or network. This research paper intends to develop a better classification method for this metamorphic malware using the latest malware API calls dataset. The multi-class malware classification used in this study is gated recurrent units (GRU). Another non-conventional multi-class malware classification method used is convolution neural network with long short-term memory (CNN + LSTM). The multi-classification results obtained by GRU are 55% with a 0.56 F1-score, and CNN + LSTM is 60% with a 0.61 F1-score, which is quite good. Moreover, the performance of the proposed deep learning models is compared against different classifiers and existing models to show their effectiveness in categorising malware.
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