Malware detection in PE files using deep learning with self-supervised learning techniques
DOI:
https://doi.org/10.61591/jslhu.20.608Từ khóa:
Malware representation; Malware detection; Deep learning; Convolutional neural network; Self-supervised learning.Tóm tắt
In recent years, there has been a surge in new malware created by hackers globally, posing challenges for traditional detection methods. This paper explores using advanced artificial intelligence, specifically Deep Learning with Self-supervised learning, to identify malware in executable files. Our study focuses on comparing the effectiveness of popular deep learning techniques like CNN models and fine-tuned CNN models, against Autoencoder models. The key contribution of this paper lies in comparing the results of these different approaches to malware detection.
Tài liệu tham khảo
Anh Tran Ngoc, Linh Vo Khuong. Malware detection based on Machine Learning and PE header information. Information Security Journal 2021. Vietnam.
Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. International Conference on Neural Information Processing Systems (NIPS) 2012.
Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister. CutPaste: Self-Supervised Learning for Anomaly Detection and Localization, Computer Vision Foundation 2021.
Gibert D. Convolutional neural networks for malware classification. University Rovira i Virgili, Tarragona, Spain, 2016.
Hung Nguyen Viet, Ngoc Quach Danh, Dung Pham Ngoc. Research on techniques of representing malware files and deep learning models in malware detection, XXII National Conference: Some selected issues of Information and Communication Technology 2019. Thai Binh, Vietnam.
Kephart J.O. Tesauro, G.J., Gregory B Sorkin. Neural networks for computer virus recognition. IEEE International Conference on Intelligence and Security Informatics 1996.
Hadi Hojjati, Thi Kieu Khanh Ho, Naregs Armanfard, Self-Supervised Anomaly Detection: A Survey and Outlook 2023. Montreal, QC, Canada,
Hyrum S. Aderson, Phil Roth. EMBER: An open dataset for training static PE malware machine learning models, arXivLabs, Cornell University 2018.
L. Nataraj, S. Karthikeyan, G. Jacob, and B. S. Manjunath. Malware images: Visualization and automatic classification, Proceedings of the 8th International Symposium on Visualization for Cyber Security 2011.
Linh V. K., Hung Ng. V., Anh Tr. Ng. Enhance Deep Learning model for malware detection with a new image representation method, Information Security Journal 2023., Vietnam.
Li Deng George E. Dahl, Jack W. Stokes and Dong Yu. Large-scale malware classification using random projections and neural network 2013. ICASSP.
Moreira, C. C., Moreira, D. C., & de Sales Jr, C. D. S. Improving ransomware detection based on portable executable header using xception convolutional neural network, Computers & Security 2023. 130, 103265.
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting J. Mach. Learn 2013. Res. 15(1):1929–1958.
Razvan Pascanu, Jack W. Stokes, Li Deng, Dong Yu, Mady Marinescu, Anil Thomas. Malware Classification with Recurrent Networks 2015. IEEE ICASSP.
Ren, Z., Chen, G., & Lu, W. Malware visualization methods based on deep convolution neural networks, Multimedia Tools and Applications 2020. 79, 10975-10993.
Sunoh Choi, Sungwook Jang, Youngsoo Kim, Jonghyun Kim. Malware Detection using Malware Image and Deep Learning. International Conference on Information and Communication Technology Convergence 2017, Jeju, Korea (South).
Seonhee Seok, Howon Kim. Visualized Malware Classification Based on Convolutional Network. Journal of The Korea Institute of Information Security and Cryptology 2016.
Setia Juli Irzal Ismail, Hafiz Pradana Gemilang, Budi Rahardjo, Hendrawan. Self-Supervised Learning Implementation for Malware Detection. International Conference on Wireless and Telematics (ICWT) 2022.
Tu Nguyen Minh, Hung Nguyen Viet, Anh Phan Viet, Loi Cao Van, Nathan Shone. Detecting Malware Based on Dynamic Analysis Techniques Using Deep Graph Learning. Lecture Notes in Computer Science 2020, vol. 12466.
VirusShare – free malware storage, https://virusshare.com/. Accessed: 2023-05-01.
Virustotal – free online malware scanner,
https://www.virustotal.com/. Accessed: 2023-04-30.
Website Center for Internet Security CIS - A community-driven nonprofit, responsible for the CIS Controls and CIS Benchmarks, https://www.cisecurity.org/. Accessed: 2023-08-23
Wenyi Huang, Jack W. Stokes, MtNet: A Multi-Task Neural Network for Dynamic Malware Classification, DIMVA, 2016.
Xiaofei Xing, Xiang Jin, Haroon Elahi, Hai Jiang, Guojun Wang, A Malware Detection Approach Using Autoencoder in Deep Learning, IEEE Access 2022. Vol. 10.
Xin Li, Peixin Lu, Lianting Hu, XiaoGuang Wang, Long Lu. A novel self-learning semi-supervised deep learning network to detect fake news on social media. Multimedia Tools and Applications 2022.
