Visible to the public Classifying Malware Using Convolutional Gated Neural Network

TitleClassifying Malware Using Convolutional Gated Neural Network
Publication TypeConference Paper
Year of Publication2018
AuthorsKim, C. H., Kabanga, E. K., Kang, S.
Conference Name2018 20th International Conference on Advanced Communication Technology (ICACT)
Date Publishedfeb
KeywordsCNN, convolutional gated recurrent neural network model, convolutional neural networks, Deep Neural Network, feedforward neural nets, Gated Recurrent Unit, Human Behavior, information technology society, invasive software, Logic gates, machine learning, malicious software, Malware, malware classification, malware detection, Metrics, microsoft malware classification challenge, Neural Network, pattern classification, privacy, pubcrawl, recurrent neural nets, Recurrent neural networks, resilience, Resiliency, Task Analysis
Abstract

Malware or Malicious Software, are an important threat to information technology society. Deep Neural Network has been recently achieving a great performance for the tasks of malware detection and classification. In this paper, we propose a convolutional gated recurrent neural network model that is capable of classifying malware to their respective families. The model is applied to a set of malware divided into 9 different families and that have been proposed during the Microsoft Malware Classification Challenge in 2015. The model shows an accuracy of 92.6% on the available dataset.

URLhttps://ieeexplore.ieee.org/document/8323640
DOI10.23919/ICACT.2018.8323640
Citation Keykim_classifying_2018