International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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BM3D and Deep Image Prior based Denoising for the Defense against Adversarial Attacks on Malware Detection Networks

  • Sandra, Kumi (Department of Computer Engineering, Dongseo University) ;
  • Lee, Suk-Ho (Department of Computer Engineering, Dongseo University)
  • Received : 2021.08.12
  • Accepted : 2021.08.20
  • Published : 2021.09.30
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Abstract

Recently, Machine Learning-based visualization approaches have been proposed to combat the problem of malware detection. Unfortunately, these techniques are exposed to Adversarial examples. Adversarial examples are noises which can deceive the deep learning based malware detection network such that the malware becomes unrecognizable. To address the shortcomings of these approaches, we present Block-matching and 3D filtering (BM3D) algorithm and deep image prior based denoising technique to defend against adversarial examples on visualization-based malware detection systems. The BM3D based denoising method eliminates most of the adversarial noise. After that the deep image prior based denoising removes the remaining subtle noise. Experimental results on the MS BIG malware dataset and benign samples show that the proposed denoising based defense recovers the performance of the adversarial attacked CNN model for malware detection to some extent.

Keywords

Acknowledgement

This work was supported by Dongseo University, "Dongseo Cluster Project" Research Fund of 2021 (DSU-20210001).

References

  1. "The AV-TEST Institute." https://www.av-test.org/en/statistics/malware/ (accessed Nov. 30, 2020).
  2. Malwarebytes Labs, "2020 State of Malware Report." https://resources.malwarebytes.com/files/2020/02/2020_State-of-Malware-Report.pdf (accessed Nov. 30, 2020).
  3. "What is malware and why do cybercriminals use malware?" https://www.mcafee.com/en-us/antivirus/malware.html (accessed Nov. 30, 2020).
  4. Avira Protection Lab, "Malware Threat Report: Q2 2020 Statistics and Trends." https://www.avira.com/en/blog/malware-threat-report-q2-2020-statistics-and-trends (accessed Nov. 30, 2020).
  5. "THE STATE OF RANSOMWARE 2020." https://secure2.sophos.com/en-us/content/state-of-ransomware.aspx (accessed Nov. 30, 2020).
  6. P. Faruki, V. Ganmoor, V. Laxmi, M. S. Gaur, and A. Bharmal, "AndroSimilar: Robust statistical feature signature for android malware detection, " in Proc. 6th International Conference on Security of Information and Networks, pp. 152-159, Nov. 26-28, 2013, https://doi.org/10.1145/2523514.2523539.
  7. M. Christodorescu and S. Jha, "Static analysis of executables to detect malicious patterns," in Proc. 12th conference on USENIX Security Symposium, pp. 12-13, Aug. 4-6, 2003,
  8. M. Egele, T. Scholte, E. Kirda, and C. Kruegel, "A survey on automated dynamic malware-analysis techniques and tools," ACM Computing Surveys, Vol.44,No.2,pp. 1-42, Feb. 2012, https://doi.org/10.1145/2089125.2089126.
  9. J. Scott, "Signature Based Malware Detection is Dead," Cybersecurity Think Tank, Inst. Crit. Infrastruct. Technol., no. February, 2017.
  10. A. Moser, C. Kruegel, and E. Kirda, "Limits of static analysis for malware detection," in Proc. 23rd Annual Computer Security Applications Conference, Dec. 10-14, 2007, https://doi.org/10.1109/ACSAC.2007.21
  11. P. V. Shijo and A. Salim, "Integrated static and dynamic analysis for malware detection," Procedia Computer Science, Vol. 46, pp. 804-811, Dec. 2015, https://doi.org/10.1016/j.procs.2015.02.149.
  12. S. Tobiyama, Y. Yamaguchi, H. Shimada, T. Ikuse, and T. Yagi, "Malware Detection with Deep Neural Network Using Process Behavior," in Proc. IEEE 40th Annual Computer Software and Applications Conference, June 10-14, Vol. 2, 2016, https://doi.org/10.1109/COMPSAC.2016.151.
  13. A. Mohaisen, O. Alrawi, and M. Mohaisen, "AMAL: High-fidelity, behavior-based automated malware analysis and classification," Comput. Secur., Vol. 52, 2015, https://doi.org/10.1016/j.cose.2015.04.001.
  14. L. Nataraj, S. Karthikeyan, G. Jacob, and B. S. Manjunath, "Malware images: Visualization and automatic classification," Proc. 8th International Symposium on Visualization for Cyber Security, pp.1-7, July 20, 2011, https://doi.org/10.1145/2016904.2016908.
  15. M. Kalash, M. Rochan, N. Mohammed, N. D. B. Bruce, Y. Wang, and F. Iqbal, "Malware Classification with Deep Convolutional Neural Networks," in Proc. 2018 9th IFIP International Conference on New Technologies, Mobility and Security, Feb. 26-28, 2018, https://doi.org/10.1109/NTMS.2018.8328749.
  16. J. Lee and S.-J. Shin, "A Study of Video-Based Abnormal Behavior Recognition Model Using Deep Learning," International journal of advanced smart convergence, Vol. 9, No. 4, pp. 115-119, Dec. 2020. https://doi.org/10.7236/IJASC.2020.9.4.115
  17. B. Kim and J. Heo, "Semi-Supervised Learning Based Anomaly Detection for License Plate OCR in Real Time Video," International journal of advanced smart convergence, Vol. 9, No. 1, pp. 113-120, Mar. 2020. https://doi.org/10.7236/IJASC.2020.9.1.113
  18. Y. Lee and J. Shim, "Deep Learning and Color Histogram based Fire and Smoke Detection Research," International journal of advanced smart convergence, Vol. 8, No. 2, pp. 116-125, Jun. 2019. https://doi.org/10.7236/IJASC.2019.8.2.116
  19. I. J. Goodfellow, J. Shlens, and C. Szegedy, "Explaining and harnessing adversarial examples," in Proc. 3rd International Conference on Learning Representations (ICLR), May 7-9, 2015.
  20. X. Liu, J. Zhang, Y. Lin, and H. Li, "ATMPA: Attacking machine learning-based malware visualization detection methods via adversarial examples," in Proc. 2019 IEEE/ACM 27th International Symposium on Quality of Service, June 24-25, 2019, https://doi.org/10.1145/3326285.3329073.
  21. R. Ronen, M. Radu, C. Feuerstein, E. Yom-Tov, and M. Ahmadi, "Microsoft Malware Classification Challenge," CoRR, vol. abs/1802.1, 2018, [Online]. Available: https://arxiv.org/abs/1802.10135.
  22. M.-I. Nicolae et al., "Adversarial Robustness Toolbox v1.0.0," arXiv, 2018, [Online]. Available: https://arxiv.org/abs/1807.01069.
  23. K. Dabov, A. Foi; V. Katkovnik, K. Egiazarian, "Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering," IEEE Trans. on Image Processing, Vol. 16, No. 8, pp. 2080 - 2095, Aug. 2007. https://doi.org/10.1109/TIP.2007.901238
  24. D. Ulyanov, A. Vedaldi, V. Lempitsky, "Deep Image Prior, " in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9446-9454, June 18-22, 2018. https://doi.org/10.1007/s11263-020-01303-4