Abstract
The biggest problem with recovering from cyberattacks is that security professionals rarely get the chance to deal with them immediately. So, using advanced intelligent techniques, we can defense systems against malware the moment it begins to download. For that reason, a new type of feature has been recently introduced for malware classification task, borrowing techniques from computer vision community called malware visualization technique. Malware classification goal is to know how they work, and then we can rapidly defend them especially in the case of zero-days attacks. In this paper, we adopt KNN algorithm to classify malwares based on their image visualization. So, a malware binary is converted to grayscale image. Then to extract similarities and dis-similarities from these images a GIST descriptor is computed. We used a database of 9339 samples of malwares belonging to 25 families. Our malware classifier reached a high score of 97%, which is very close to the results found in literature.
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References
Cakir, B., Dogdu, E.: Malware classification using deep learning methods. In: Proceedings of the ACMSE 2018 Conference, pp. 10:1–10:5. ACM, New York (2018). https://doi.org/10.1145/3190645.3190692
Islam, R., et al.: Classification of malware based on integrated static and dynamic features. J. Netw. Comput. Appl. 36(2), 646–656 (2013). https://doi.org/10.1016/j.jnca.2012.10.004
Jerlin, M.A., Marimuthu, K.: A new malware detection system using machine learning techniques for API call sequences. J. Appl. Secur. Res. 13(1), 45–62 (2018). https://doi.org/10.1080/19361610.2018.1387734
Luo, J., Lo, D.C.: Malware image classification using machine learning with local binary pattern. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 4664–4667 (2017). https://doi.org/10.1109/BigData.2017.8258512
Makandar, A., Patrot, A.: Malware analysis and classification using artificial neural network. In: 2015 International Conference on Trends in Automation, Communications and Computing Technology (I-TACT-15), pp. 1–6 (2015). https://doi.org/10.1109/ITACT.2015.7492653
Makandar, A., Patrot, A.: Malware class recognition using image processing techniques. In: 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI), pp. 76–80 (2017). https://doi.org/10.1109/ICDMAI.2017.8073489
Makandar, A., Patrot, A.: Wavelet statistical feature based malware class recognition and classification using supervised learning classifier. Orient. J. Comput. Sci. Technol. 10(2), 400–406 (2017). https://doi.org/10.13005/ojcst/10.02.20
Nataraj, L., et al.: Malware images: visualization and automatic classification. In: Proceedings of the 8th International Symposium on Visualization for Cyber Security - VizSec 2011, pp. 1–7. ACM, Pittsburgh (2011). https://doi.org/10.1145/2016904.2016908
Parsian, M.: Data Algorithms. O’Reilly, Boston (2015)
Rieck, K., et al.: Learning and classification of malware behavior. In: Zamboni, D. (ed.) Detection of Intrusions and Malware, and Vulnerability Assessment, pp. 108–125. Springer, Berlin (2008). https://doi.org/10.1007/978-3-540-70542-0_6
Cisco 2018 Annual Cybersecurity Report
Le rapport de sécurité 2018/2019 d’AV-TEST (2019)
Acknowledgments
We acknowledge financial support for this research from the “Centre National pour la Recherche Scientifique et technique”, CNRST, Morocco.
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Ben Abdel Ouahab, I., Bouhorma, M., Boudhir, A.A., El Aachak, L. (2020). Classification of Grayscale Malware Images Using the K-Nearest Neighbor Algorithm. In: Ben Ahmed, M., Boudhir, A., Santos, D., El Aroussi, M., Karas, İ. (eds) Innovations in Smart Cities Applications Edition 3. SCA 2019. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-030-37629-1_75
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DOI: https://doi.org/10.1007/978-3-030-37629-1_75
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