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

Year 2025, Volume: 14 Issue: 4, 2417 - 2437, 31.12.2025

Birkan Büyükarıkan

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

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

Image-Based Malware , Shallow convolutional neural networks , Efficient channel attention , Detection.

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.

References

• Y. Ye, T. Li, D. Adjeroh, and S. S. Iyengar, “A survey on malware detection using data mining techniques,” ACM Computing Surveys, vol. 50, no. 3, pp. 1–40, 2017.
• G. A. Sharma, K. J. Singh, and M. D. Singh, “A deep learning approach to image-based malware analysis,” in Progress in Computing, Analytics and Networking (ICCAN 2019). Springer, 2020, pp. 327–339.
• S. Madan, S. Sofat, and D. Bansal, “Tools and techniques for collection and analysis of Internet-of-Things malware: A systematic state-of-the-art review,” Journal of King Saud University – Computer and Information Sciences, vol. 34, no. 10, pp. 9867–9888, 2022.
• R. Mitsuhashi and T. Shinagawa, “Exploring optimal deep learning models for image-based malware variant classification,” in Proc. IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), 2022, pp. 779–788.
• B. Zou, C. Cao, F. Tao, and L. Wang, “IMCLNet: A lightweight deep neural network for image-based malware classification,” Journal of Information Security and Applications, vol. 70, p. 103313, 2022.
• A. Hota, S. Panja, and A. Nag, “Lightweight CNN-based malware image classification for resource-constrained applications,” Innovations in Systems and Software Engineering, vol. 21, pp. 1–14, 2025.
• D. Analytics, “A signature-based analytic system to collect, extract, analyze and associate Android malware,” in Proc. IEEE Int. Conf. Trust, Security and Privacy in Computing and Communications (TrustCom), 2013, pp. 163–171.
• M. I. Sharif, A. Lanzi, J. T. Giffin, and W. Lee, “Impeding malware analysis using conditional code obfuscation,” in Proc. Network and Distributed System Security Symposium (NDSS), 2008.
• Y. Feng, S. Anand, I. Dillig, and A. Aiken, “Apposcopy: Semantics-based detection of Android malware through static analysis,” in Proc. 22nd ACM SIGSOFT Int. Symp. Foundations of Software Engineering, 2014, pp. 576–587.
• P. Ferrie, “Attacks on virtual machine emulators,” Symantec Security Response, vol. 5, pp. 1–13, 2006.
• T. Raffetseder, C. Kruegel, and E. Kirda, “Detecting system emulators,” in Proc. Int. Conf. Information Security (ISC). Springer, 2007, pp. 1–18.
• D. Kirat and G. Vigna, “Malgene: Automatic extraction of malware analysis evasion signature,” in Proc. ACM SIGSAC Conf. Computer and Communications Security, 2015, pp. 769–780.
• A. Mahindru and A. L. Sangal, “MLDroid—Framework for Android malware detection using machine learning techniques,” Neural Computing and Applications, vol. 33, no. 10, pp. 5183–5240, 2021.
• J. Singh and J. Singh, “A survey on machine learning-based malware detection in executable files,” Journal of Systems Architecture, vol. 112, p. 101861, 2021.
• S. K. Smmarwar, G. P. Gupta, and S. Kumar, “A hybrid feature selection approach-based Android malware detection framework using machine learning techniques,” in Cyber Security, Privacy and Networking (ICSPN 2021). Springer, 2022, pp. 347–356.
• J. Hemalatha, S. A. Roseline, S. Geetha, S. Kadry, and R. Damaševičius, “An efficient DenseNet-based deep learning model for malware detection,” Entropy, vol. 23, no. 3, p. 344, 2021.
• T. M. Mohammed, L. Nataraj, S. Chikkagoudar, S. Chandrasekaran, and B. Manjunath, “Malware detection using frequency domain-based image visualization and deep learning,” arXiv preprint arXiv:2101.10578, 2021.
• M. Basak and M.-M. Han, “CyberSentinel: A transparent defense framework for malware detection in high-stakes operational environments,” Sensors, vol. 24, no. 11, p. 3406, 2024.
• L. Sasikala and C. Shanmuganathan, “Effective malware classification using fine-tuned CNN architecture: An image-based approach,” in Proc. Int. Conf. Networking and Communications (ICNWC), IEEE, 2024, pp. 1–6.
• S. K. Smmarwar, G. P. Gupta, and S. Kumar, “Design of a fused triple convolutional neural network for malware detection: A visual classification approach,” in Advances in Computing and Data Sciences (ICACDS 2021). Springer, 2021, pp. 279–289.
• C. Wang, Z. Zhao, F. Wang, and Q. Li, “A novel malware detection and family classification scheme for IoT based on DEAM and DenseNet,” Security and Communication Networks, vol. 2021, p. 6658842, 2021.
• S. K. Majhi, A. Panda, S. K. Srichandan, U. Desai, and B. Acharya, “Malware image classification: Comparative analysis of a fine-tuned CNN and pre-trained models,” International Journal of Computers and Applications, vol. 45, no. 11, pp. 709–721, 2023.
• P. Panda, O. K. CU, S. Marappan, S. Ma, and D. Veesani Nandi, “Transfer learning for image-based malware detection for IoT,” Sensors, vol. 23, no. 6, p. 3253, 2023.
• Z. Cui, F. Xue, X. Cai, Y. Cao, G.-G. Wang, and J. Chen, “Detection of malicious code variants based on deep learning,” IEEE Transactions on Industrial Informatics, vol. 14, no. 7, pp. 3187–3196, 2018.
• D. Vasan, M. Alazab, S. Wassan, H. Naeem, B. Safaei, and Q. Zheng, “IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture,” Computer Networks, vol. 171, p. 107138, 2020.
• F. N. Naseem, A. Aris, L. Babun, E. Tekiner, and A. S. Uluagac, “MINOS: A lightweight real-time cryptojacking detection system,” in Proc. Network and Distributed System Security Symposium (NDSS), 2021.
• M. Cho, J.-S. Kim, J. Shin, and I. Shin, “Mal2D: 2D-based deep learning model for malware detection using black-and-white binary image,” IEICE Transactions on Information and Systems, vol. 103, no. 4, pp. 896–900, 2020.
• P. S. Dharmalaksana, T. Mantoro, L. Khakim, and M. Nurseno, “Improved malware detection results using visualization-based detection techniques and convolutional neural network,” in Proc. IEEE Int. Conf. Computing, Engineering and Design (ICCED), 2022, pp. 1–5.
• A. M. Alnajim, S. Habib, M. Islam, R. Albelaihi, and A. Alabdulatif, “Mitigating the risks of malware attacks with deep learning techniques,” Electronics, vol. 12, no. 14, p. 3166, 2023.
• R. Chopra, S. Acharya, U. Rawat, and R. Bhatnagar, “An energy-efficient, robust, sustainable, and low computational cost method for mobile malware detection,” Applied Computational Intelligence and Soft Computing, vol. 2023, p. 2029064, 2023.
• J. Su, D. V. Vasconcellos, S. Prasad, D. Sgandurra, Y. Feng, and K. Sakurai, “Lightweight classification of IoT malware based on image recognition,” in Proc. IEEE 42nd Annual COMPSAC, 2018, vol. 2, pp. 664–669.
• B. Yuan, J. Wang, P. Wu, and X. Qing, “IoT malware classification based on lightweight convolutional neural networks,” IEEE Internet of Things Journal, vol. 9, no. 5, pp. 3770–3783, 2021.
• K. Shaukat, S. Luo, and V. Varadharajan, “A novel deep learning-based approach for malware detection,” Engineering Applications of Artificial Intelligence, vol. 122, p. 106030, 2023.
• N. Younas, S. Riaz, S. Ali, R. Khan, F. Ali, and D. Kwak, “Detecting malicious code variants using convolutional neural network with transfer learning,” PeerJ Computer Science, vol. 11, p. e2727, 2025.
• T. Van Dao, H. Sato, and M. Kubo, “An attention mechanism for combination of CNN and VAE for image-based malware classification,” IEEE Access, vol. 10, pp. 85127–85136, 2022.
• H. Huang, R. Du, Z. Wang, X. Li, and G. Yuan, “A malicious code detection method based on stacked depthwise separable convolutions and attention mechanism,” Sensors, vol. 23, no. 16, p. 7084, 2023.
• G. Alandjani, “Securing edge devices: Malware classification with dual-attention deep network,” Applied Sciences, vol. 14, no. 11, p. 4645, 2024.
• J. Chen, M. Wu, W. Gan, H. Huang, and T. S. C. Lau, “A lightweight CNN malware classification method for software detection,” International Journal of Machine Learning and Cybernetics, pp. 1–19, 2025.
• D. Zhang, Y. Song, Q. Xiang, and Y. Wang, “IMCMK-CNN: A lightweight convolutional neural network with multi-scale kernels for image-based malware classification,” Alexandria Engineering Journal, vol. 111, pp. 203–220, 2025.
• J. Tang et al., “Android malware detection based on a novel mixed bytecode image combined with attention mechanism,” Journal of Information Security and Applications, vol. 82, p. 103721, 2024.
• Kaggle, “Malware Detection with Images,” [Online]. Available: https://www.kaggle.com/datasets/kokykg/malware-detection-with-images/data . Accessed: Dec. 22, 2025.
• L. Nataraj, S. Karthikeyan, G. Jacob, and B. S. Manjunath, “Malware images: Visualization and automatic classification,” in Proc. Int. Symp. Visualization for Cyber Security, 2011, pp. 1–7.
• GitHub, “Benign Images,” [Online]. Available: https://github.com/cridin1/malware-classification-CNN/tree/main/benign_data/benign_imgs . Accessed: Dec. 22, 2025.
• Kaggle, “Fusion Malware Dataset,” [Online]. Available: https://www.kaggle.com/datasets/marcesalas/fusion-dataset-59-malware-families-in-png-format . Accessed: Dec. 22, 2025.
• Kaggle, “Malware Images,” [Online]. Available: https://www.kaggle.com/datasets/walt30/malware-images/data . Accessed: Dec. 22, 2025.
• M. Omar, “New approach to malware detection using optimized convolutional neural network,” in Machine Learning for Cybersecurity: Innovative Deep Learning Solutions. Springer, 2022, pp. 13–35.