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Malware Classification and Analysis Using Convolutional and Recurrent Neural Network

Malware Classification and Analysis Using Convolutional and Recurrent Neural Network

Yassine Maleh
Copyright: © 2019 |Pages: 23
ISBN13: 9781522578628|ISBN10: 1522578625|EISBN13: 9781522578635
DOI: 10.4018/978-1-5225-7862-8.ch014
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MLA

Maleh, Yassine. "Malware Classification and Analysis Using Convolutional and Recurrent Neural Network." Handbook of Research on Deep Learning Innovations and Trends, edited by Aboul Ella Hassanien, et al., IGI Global, 2019, pp. 233-255. https://doi.org/10.4018/978-1-5225-7862-8.ch014

APA

Maleh, Y. (2019). Malware Classification and Analysis Using Convolutional and Recurrent Neural Network. In A. Hassanien, A. Darwish, & C. Chowdhary (Eds.), Handbook of Research on Deep Learning Innovations and Trends (pp. 233-255). IGI Global. https://doi.org/10.4018/978-1-5225-7862-8.ch014

Chicago

Maleh, Yassine. "Malware Classification and Analysis Using Convolutional and Recurrent Neural Network." In Handbook of Research on Deep Learning Innovations and Trends, edited by Aboul Ella Hassanien, Ashraf Darwish, and Chiranji Lal Chowdhary, 233-255. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7862-8.ch014

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Abstract

Over the past decade, malware has grown exponentially. Traditional signature-based approaches to detecting malware have proven their limitations against new malware, and categorizing malware samples has become essential to understanding the basics of malware behavior. Recently, antivirus solutions have increasingly started to adopt machine learning approaches. Unfortunately, there are few open source data sets available for the academic community. One of the largest data sets available was published last year in a competition on Kaggle with data provided by Microsoft for the big data innovators gathering. This chapter explores the problem of malware classification. In particular, this chapter proposes an innovative and scalable approach using convolutional neural networks (CNN) and long short-term memory (LSTM) to assign malware to the corresponding family. The proposed method achieved a classification accuracy of 98.73% and an average log loss of 0.0698 on the validation data.

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