API Call-Based Malware Classification Using Recurrent Neural Networks

Authors

  • Chen Li Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan https://orcid.org/0000-0002-8784-8148
  • Junjun Zheng Department of Information Science and Engineering, Ritsumeikan University, Kusatsu, 525-8577, Japan https://orcid.org/0000-0002-5529-1429

DOI:

https://doi.org/10.13052/jcsm2245-1439.1036

Keywords:

Malware classification, API call sequence, Recurrent neural network, Long short-term memory, Gated recurrent unit (GRU)

Abstract

Malicious software, called malware, can perform harmful actions on computer systems, which may cause economic damage and information leakage. Therefore, malware classification is meaningful and required to prevent malware attacks. Application programming interface (API) call sequences are easily observed and are good choices as features for malware classification. However, one of the main issues is how to generate a suitable feature for the algorithms of classification to achieve a high classification accuracy. Different malware sample brings API call sequence with different lengths, and these lengths may reach millions, which may cause computation cost and time complexities. Recurrent neural networks (RNNs) is one of the most versatile approaches to process time series data, which can be used to API call-based Malware calssification. In this paper, we propose a malware classification model with RNN, especially the long short-term memory (LSTM) and the gated recurrent unit (GRU), to classify variants of malware by using long-sequences of API calls. In numerical experiments, a benchmark dataset is used to illustrate the proposed approach and validate its accuracy. The numerical results show that the proposed RNN model works well on the malware classification.

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Author Biographies

Chen Li, Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan

Chen Li received the B.S. degree in computer engineering from Qingdao College, Ocean University of China, in 2012, and the M.S. and D.Eng. degrees in engineering from Hiroshima University, Higashihiroshima, Japan, in 2016 and 2019, respectively. In 2019 and 2020, he was a Visiting Researcher with the Graduate School of Advanced Science and Engineering, Hiroshima University, Japan. Since 2021, he has been a post-doctoral researcher with the Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Japan. His research interests include performance evaluation, data mining and deep learning.

Junjun Zheng, Department of Information Science and Engineering, Ritsumeikan University, Kusatsu, 525-8577, Japan

Junjun Zheng received the B.S.E. degree in engineering from Fujian Normal University, Fuzhou, China, in 2010, and the M.S. and D.Eng. degrees in engineering from Hiroshima University, Higashihiroshima, Japan, in 2013 and 2016, respectively. In 2016 and 2017, he was a Visiting Researcher with the Department of Information Engineering, Graduate School of Engineering, Hiroshima University. Since 2018, he has been an Assistant Professor with the Department of Information Science and Engineering, Ritsumeikan University, Japan. His research interests include performance evaluation and dependable computing.

Zheng is a member of the Operations Research Society of Japan, the Reliability Engineering Association of Japan, the Institute of Electrical, Information and Communication Engineers, and the Institute of Electrical and Electronics Engineers.

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Published

2021-05-27

Issue

Section

Emerging Trends in Cyber Security and Cryptography