Next Article in Journal
Monitoring Wheat Growth Using a Portable Three-Band Instrument for Crop Growth Monitoring and Diagnosis
Previous Article in Journal
Computationally Efficient Wildfire Detection Method Using a Deep Convolutional Network Pruned via Fourier Analysis
Open AccessArticle

Attention-Based Automated Feature Extraction for Malware Analysis

Department of Computer Engineering, Honam University, Gwangju 62399, Korea
Department of Computer Science and Engineering, Kangwon University, Kangwon-do 24341, Korea
Information Security Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
Author to whom correspondence should be addressed.
Sensors 2020, 20(10), 2893;
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
Show Figures

Figure 1

MDPI and ACS Style

Choi, S.; Bae, J.; Lee, C.; Kim, Y.; Kim, J. Attention-Based Automated Feature Extraction for Malware Analysis. Sensors 2020, 20, 2893.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop