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Open AccessArticle

Attention-Based Automated Feature Extraction for Malware Analysis

1
Department of Computer Engineering, Honam University, Gwangju 62399, Korea
2
Department of Computer Science and Engineering, Kangwon University, Kangwon-do 24341, Korea
3
Information Security Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(10), 2893; https://doi.org/10.3390/s20102893
Received: 13 March 2020 / Revised: 12 May 2020 / Accepted: 16 May 2020 / Published: 20 May 2020
(This article belongs to the Section Intelligent Sensors)
Every day, hundreds of thousands of malicious files are created to exploit zero-day vulnerabilities. Existing pattern-based antivirus solutions face difficulties in coping with such a large number of new malicious files. To solve this problem, artificial intelligence (AI)-based malicious file detection methods have been proposed. However, even if we can detect malicious files with high accuracy using deep learning, it is difficult to identify why files are malicious. In this study, we propose a malicious file feature extraction method based on attention mechanism. First, by adapting the attention mechanism, we can identify application program interface (API) system calls that are more important than others for determining whether a file is malicious. Second, we confirm that this approach yields an accuracy that is approximately 12% and 5% higher than a conventional AI-based detection model using convolutional neural networks and skip-connected long short-term memory-based detection model, respectively. View Full-Text
Keywords: malware analysis; deep learning; attention malware analysis; deep learning; attention
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Choi, S.; Bae, J.; Lee, C.; Kim, Y.; Kim, J. Attention-Based Automated Feature Extraction for Malware Analysis. Sensors 2020, 20, 2893.

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