Shallow Convolutional Neural Network with Efficient Channel Attention for Image-Based Malware Detection

Birkan Büyükarıkan

doi:10.17798/bitlisfen.1740924

Shallow Convolutional Neural Network with Efficient Channel Attention for Image-Based Malware Detection

Abstract

Malware software, which is designed to malware computer systems, steal personal data, and gain illegal access, is one of the primary cyberthreats. The inability of traditional methods to detect such software has led to the development of more robust and innovative strategies. Image-based malware detection techniques have become much more common in recent years. These techniques use Convolutional Neural Networks (CNNs) to identify image malware. The aim of the study is to classify malware with a hybrid model combining Shallow CNN and Efficient Channel Attention (ECA) mechanism. The study used a public dataset. Grayscale images in this dataset were converted to RGB color space using a Pseudocoloring technique. The study was evaluated using a 5-fold cross-validation method. The Shallow CNN-ECA model had an accuracy of 0.983. Additionally, with an accuracy of 0.979, the Shallow CNN model ranked second among the suggested techniques. According to experimental results, the proposed model outperformed well-known lightweight CNN methods.

Keywords

Ethical Statement

As no human, animal, or sensitive data were involved in this study, ethical approval was not applicable. Thus, ethical approval was not requested or needed.

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Details

Primary Language

English

Subjects

Signal Processing

Journal Section

Research Article

Authors

Birkan Büyükarıkan ^*
0000-0002-9703-9678
Türkiye

Publication Date

December 31, 2025

Submission Date

July 12, 2025

Acceptance Date

October 10, 2025

Published in Issue

Year 2025 Volume: 14 Number: 4

DOI

https://doi.org/10.17798/bitlisfen.1740924

IZ

https://izlik.org/JA82WR73TA

Cite

RIS / Bibtex

APA

Büyükarıkan, B. (2025). Shallow Convolutional Neural Network with Efficient Channel Attention for Image-Based Malware Detection. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 14(4), 2417-2437. https://doi.org/10.17798/bitlisfen.1740924

AMA

1.Büyükarıkan B. Shallow Convolutional Neural Network with Efficient Channel Attention for Image-Based Malware Detection. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2025;14(4):2417-2437. doi:10.17798/bitlisfen.1740924

Chicago

Büyükarıkan, Birkan. 2025. “Shallow Convolutional Neural Network With Efficient Channel Attention for Image-Based Malware Detection”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 14 (4): 2417-37. https://doi.org/10.17798/bitlisfen.1740924.

EndNote

Büyükarıkan B (December 1, 2025) Shallow Convolutional Neural Network with Efficient Channel Attention for Image-Based Malware Detection. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 14 4 2417–2437.

IEEE

[1]B. Büyükarıkan, “Shallow Convolutional Neural Network with Efficient Channel Attention for Image-Based Malware Detection”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 14, no. 4, pp. 2417–2437, Dec. 2025, doi: 10.17798/bitlisfen.1740924.

ISNAD

Büyükarıkan, Birkan. “Shallow Convolutional Neural Network With Efficient Channel Attention for Image-Based Malware Detection”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 14/4 (December 1, 2025): 2417-2437. https://doi.org/10.17798/bitlisfen.1740924.

JAMA

1.Büyükarıkan B. Shallow Convolutional Neural Network with Efficient Channel Attention for Image-Based Malware Detection. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2025;14:2417–2437.

MLA

Büyükarıkan, Birkan. “Shallow Convolutional Neural Network With Efficient Channel Attention for Image-Based Malware Detection”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 14, no. 4, Dec. 2025, pp. 2417-3, doi:10.17798/bitlisfen.1740924.

Vancouver

1.Birkan Büyükarıkan. Shallow Convolutional Neural Network with Efficient Channel Attention for Image-Based Malware Detection. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2025 Dec. 1;14(4):2417-3. doi:10.17798/bitlisfen.1740924