banner
Home > Open Journal Systems

A recent survey of image-based malware classification using convolution neural network

Kennedy E. Ketebu, Gregory O. Onwodi, Kingsley Eghonghon Ukhurebor, Benjamin Maxwell Eneche, Nana Kojo Yaah-Nyakko

Abstract


Despite numerous breakthroughs in creating and applying new and current approaches to malware detection and classification, the number of malware attacks on computer systems and networks is increasing. Malware authors are continually changing their operations and activities with tools or methodologies, making it tough to categorize and detect malware. Malware detection methods such as static or dynamic detection, although useful, have had challenges detecting zero-day malware and polymorphic malware. Even though machine learning techniques have been applied in this area, deep neural network models using image visualization have proven to be very effective in malware detection and classification, presenting better accuracy results. Hence, this article intends to conduct a survey showing recent works by researchers and their techniques used for malware detection and classification using convolutional neural network (CNN) models highlighting strengths, and identifying areas of potential limitations such as size of datasets and features extraction. Furthermore, a review of relevant research publications on the subject is offered, which also highlights the limitations of models and dataset availability, along with a full tabular comparison of their accuracy in malware detection and classification. Consequently, this review study will contribute to the advancement and serve as a basis for future research in the field of developing CNN models for malware detection and classification.


Keywords


convolution neural network; datasets; machine learning; malware; visualization

Full Text:

PDF

References


1. Sharma A, Gupta BB, Singh AK, et al. Orchestration of APT malware evasive manoeuvers employed for eluding anti-virus and sandbox defense. Computers & Security. 2022, 115: 102627. doi: 10.1016/j.cose.2022.102627.

2. Agrawal R, Khan L. An Experience in Enhancing Machine Learning Classifier Against Low-Entropy Packed Malwares. 2021.

3. Ghosh A. An overview article on 600% increase in Cyber Attack in 2021. 2021.

4. Gibert D, Mateu C, Planes J. The rise of machine learning for detection and classification of malware: Research developments, trends and challenges. Journal of Network and Computer Applications. 2020, 153: 102526. doi: 10.1016/j.jnca.2019.102526.

5. Moussas V, Andreatos A. Malware Detection Based on Code Visualization and Two-Level Classification. Information. 2021, 12(3): 118. doi: 10.3390/info12030118.

6. Keahey TA. Using visualization to understand big data. IBM Business Analytics Advanced Visualisation. 2013, 16.

7. Nataraj L, Karthikeyan S, Jacob G, et al. Malware images. Proceedings of the 8th International Symposium on Visualization for Cyber Security. Published online July 20, 2011. doi: 10.1145/2016904.2016908.

8. Kalash M, Rochan M, Mohammed N, et al. Malware Classification with Deep Convolutional Neural Networks. 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS). Published online February 2018. doi: 10.1109/ntms.2018.8328749.

9. Alzubaidi L, Zhang J, Humaidi AJ, et al. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data. 2021, 8(1). doi: 10.1186/s40537-021-00444-8.

10. Yuan B, Wang J, Liu D, et al. Byte-level malware classification based on markov images and deep learning. Computers & Security. 2020, 92: 101740. doi: 10.1016/j.cose.2020.101740.

11. Cakir B, Dogdu E. Malware classification using deep learning methods. Proceedings of the ACMSE 2018 Conference. Published online March 29, 2018. doi: 10.1145/3190645.3190692.

12. Kolosnjaji B, Zarras A, Webster G, et al. Deep Learning for Classification of Malware System Call Sequences. Lecture Notes in Computer Science. 2016, 137-149. doi: 10.1007/978-3-319-50127-7_11.

13. Nwankwo W, Ukhurebor KE. Web Forum and Social Media: A Model for Automatic Removal of Fake Media using Multilayered Neural Networks. International Journal of Scientific & Technology Research. 2020, 9(1): 4371-4377.

14. Le Q, Boydell O, Mac Namee B, et al. Deep learning at the shallow end: Malware classification for non-domain experts. Digital Investigation. 2018, 26: S118-S126. doi: 10.1016/j.diin.2018.04.024.

15. Lo WW, Yang X, Wang Y. An Xception Convolutional Neural Network for Malware Classification with Transfer Learning. 2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS). Published online June 2019. doi: 10.1109/ntms.2019.8763852.

16. Vasan D, Alazab M, Wassan S, et al. Image-Based malware classification using ensemble of CNN architectures (IMCEC). Computers & Security. 2020, 92: 101748. doi: 10.1016/j.cose.2020.101748.

17. Yoo S, Kim S, Kim S, et al. AI-HydRa: Advanced hybrid approach using random forest and deep learning for malware classification. Information Sciences. 2021, 546: 420-435. doi: 10.1016/j.ins.2020.08.082.

18. Kumar S. MCFT-CNN: Malware classification with fine-tune convolution neural networks using traditional and transfer learning in Internet of Things. Future Generation Computer Systems. 2021, 125: 334-351. doi: 10.1016/j.future.2021.06.029.

19. Awan MJ, Masood OA, Mohammed MA, et al. Image-Based Malware Classification Using VGG19 Network and Spatial Convolutional Attention. Electronics. 2021, 10(19): 2444. doi: 10.3390/electronics10192444.

20. Asam M, Khan SH, Jamal T, et al. Malware Classification Using Deep Boosted Learning. arXiv. 2021, arXiv:2107.04008.

21. Carletti V, Greco A, Saggese A, Vento M. Robustness evaluation of convolutional neural networks for malware classification. ITASEC 2021 Italian Conference on Cybersecurity. 2021, 2940: 414-423.

22. Lin WC, Yeh YR. Efficient Malware Classification by Binary Sequences with One-Dimensional Convolutional Neural Networks. Mathematics. 2022, 10(4): 608. doi: 10.3390/math10040608.

23. O’Shaughnessy S, Sheridan S. Image-based malware classification hybrid framework based on space-filling curves. Computers & Security. 2022, 116: 102660. doi: 10.1016/j.cose.2022.102660.

24. Schofield M, Alicioglu G, Binaco R, et al. Convolutional Neural Network for Malware Classification Based on API Call Sequence. Computer Science & Information Technology (CS & IT). Published online January 23, 2021. doi: 10.5121/csit.2021.110106.

25. Parihar AS, Kumar S, Khosla S. S-DCNN: Stacked deep convolutional neural networks for malware classification. Multimedia Tools and Applications. 2022, 81(21): 30997-31015. doi: 10.1007/s11042-022-12615-7.

26. Naeem MR, Amin R, Alshamrani SS, et al. Digital Forensics for Malware Classification: An Approach for Binary Code to Pixel Vector Transition. Computational Intelligence and Neuroscience. 2022, 2022: 1-12. doi: 10.1155/2022/6294058.

27. Ahmed M, Afreen N, Ahmed M, et al. An inception V3 approach for malware classification using machine learning and transfer learning. International Journal of Intelligent Networks. 2023, 4: 11-18. doi: 10.1016/j.ijin.2022.11.005.

28. Chen Z, Cao J. VMCTE: Visualization-Based Malware Classification Using Transfer and Ensemble Learning. Computers, Materials & Continua. 2023, 75(2): 4445-4465. doi: 10.32604/cmc.2023.038639.




DOI: https://doi.org/10.32629/jai.v7i5.1287

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Kennedy E. Ketebu, Gregory O. Onwodi, Kingsley Eghonghon Ukhurebor, Benjamin Maxwell Eneche, Nana Kojo Yaah-Nyakko

License URL: https://creativecommons.org/licenses/by-nc/4.0/