Обнаружение обфусцированных вредоносных программ в Windows с помощью методов ансамблевого обучения
Ключевые слова:
обнаружение вредоносного ПО, машинное обучение, анализ вредоносного ПО, кибербезопасностьАннотация
В эпоху Интернета и смарт-устройств обнаружение вредоносных программ стало важным фактором для безопасности системы. Обфусцированные вредоносные программы создают значительные риски для различных платформ, включая компьютеры, мобильные устройства и устройства IoT, поскольку не позволяют использовать передовые решения для обеспечения безопасности. Традиционные эвристические и сигнатурные методы часто не справляются с этими угрозами. Поэтому была предложена экономически эффективная система обнаружения с использованием анализа дампа памяти и методов ансамблевого обучения. На основе набора данных CIC-MalMem-2022 была оценена эффективность деревьев решений, градиентного бустинга деревьев, логистической регрессии, метода случайного леса и LightGBM при выявлении обфусцированных вредоносных программ. Исследование продемонстрировало превосходство методов ансамблевого обучения в повышении точности и надежности обнаружения. Кроме того, SHAP (аддитивные объяснения Шелли) и LIME (локально интерпретируемые объяснения, не зависящие от устройства модели) использовались для выяснения прогнозов модели, повышения прозрачности и надежности. Анализ выявил важные особенности, существенно влияющие на обнаружение вредоносных программ, такие как службы процессов, активные службы, дескрипторы файлов, ключи реестра и функции обратного вызова. Эти идеи имеют большое значение для совершенствования стратегий обнаружения и повышения производительности модели. Полученные результаты вносят вклад в усилия по обеспечению кибербезопасности путем всесторонней оценки алгоритмов машинного обучения для обнаружения обфусцированных вредоносных программ с помощью анализа памяти. В этой статье представлены ценные идеи для будущих исследований и достижений в области обнаружения вредоносных программ, прокладывая путь для более надежных и эффективных решений в области кибербезопасности перед лицом развивающихся и сложных вредоносных угроз.
Литература
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2. Baghirov E. Comprehensive framework for malware detection: Using ensemble methods, feature selection, and hyperparameter optimization. In 2023 IEEE 17th International Conference on Application of Information and Communication Technologies (AICT). IEEE, 2023. pp. 1–5. DOI: 10.1109/AICT59525.2023.10313179.
3. Jeon J., Jeong B., Baek S., Jeong Y.-S. Static Multi Feature-Based Malware Detection Using Multi SPP-net in Smart IoT Environments. IEEE Transactions on Information Forensics and Security. 2024. vol. 19. pp. 2487–2500. DOI: 10.1109/TIFS.2024.3350379.
4. Ismail S.J.I., Hendrawan Rahardjo B., Juhana T., Musashi Y. MalSSL – Self-Supervised Learning for Accurate and Label-Efficient Malware Classification. IEEE Access. 2024. vol. 12. pp. 58823–58835. DOI: 10.1109/ACCESS.2024.3392251.
5. Baghirov E. Malware detection based on opcode frequency. Journal of Problems of Information Technology, 2023. vol. 14(1). pp. 3–7. DOI: 10.25045/jpit.v14.i1.01.
6. Egitmen A., Yavuz A.G., Yavuz S. TRConv: Multi-Platform Malware Classification via Target Regulated Convolutions. IEEE Access. 2024. vol. 12. pp. 71492–71504. DOI: 10.1109/ACCESS.2024.3401627.
7. Gungor A., Dogru I.A., Barisci N., Toklu S. Malware detection using image-based features and machine learning methods. Journal of the Faculty of Engineering and Architecture of Gazi University. 2023. vol. 38. no. 3. pp. 1781–1792. DOI: 10.17341/gazimmfd.994289.
8. Mesbah A., Baddari I., Riahla M.A. LongCGDroid: Android malware detection through longitudinal study for machine learning and deep learning. Jordanian Journal of Computers and Information Technology. 2023. vol. 9. no. 4. pp. 328–346. DOI: 10.5455/jjcit.71-1693392249.
9. Howard A., Hope B., Saltaformaggio B., Avena E., Ahmadi M., Duncan M., McCann R., Cukierski W. Microsoft Malware Prediction. Kaggle, 2018. Available at: https://kaggle.com/competitions/microsoft-malware-prediction. (accessed 26.10.2024).
10. Ahmed I.T., Hammad B.T., Jamil N.A Comparative Performance Analysis of Malware Detection Algorithms Based on Various Texture Features and Classifiers. IEEE Access. 2024. vol. 12. pp. 11500–11519. DOI: 10.1109/ACCESS.2024.3354959.
11. Xie W., Zhang X. The Application of Machine Learning in Android Malware Detection. 2024 4th International Conference on Neural Networks, Information and Communication Engineering (NNICE). 2024. pp. 1–4. DOI: 10.1109/NNICE61279.2024.10498936.
12. Bostani H.; Moonsamy V. EvadeDroid: A practical evasion attack on machine learning for black-box Android malware detection. Computers and Security. 2024. vol. 139. DOI: 10.1016/j.cose.2023.103676.
13. Rigaki M., Garcia S. The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2024. pp. 44–64. DOI: 10.1007/978-3-031-51482-1_3.
14. Rudd E.M., Krisiloff D., Coull S., Olszewski D., Raff E., Holt J. Efficient Malware Analysis Using Metric Embeddings. Digital Threats: Research and Practice. 2024. vol. 5(1). pp. 1–20. DOI: 10.1145/3615669.
15. Zhan D., Zhang Y., Zhu L., Chen J., Xia S., Guo S., Pan Z. Enhancing reinforcement learning based adversarial malware generation to evade static detection. Alexandria Engineering Journal. 2024. vol. 98. pp. 32–43. DOI: 10.1016/j.aej.2024.04.024.
16. Aljabri M., Alhaidari F., Albuainain A., Alrashidi S., Alansari J., Alqahtani W., Alshaya J. Ransomware detection based on machine learning using memory features. Egyptian Informatics Journal. 2024. vol. 25. DOI: 10.1016/j.eij.2024.100445.
17. Ban Y., Kim M., Cho H. An Empirical Study on the Effectiveness of Adversarial Examples in Malware Detection. CMES – Computer Modeling in Engineering and Sciences. 2024. vol. 139(3). pp. 3535–3563. DOI: 10.32604/cmes.2023.046658.
18. Zhang Y., Jiang J., Yi C., Li H., Min S., Zuo R., An Z., Yu Y. A Robust CNN for Malware Classification against Executable Adversarial Attack. Electronics. 2024. vol. 13(5). DOI: 10.3390/electronics13050989.
19. Dam T.Q., Nguyen N.T., Le T.V., Le T.D., Uwizeyemungu S., Le-Dinh T. Visualizing Portable Executable Headers for Ransomware Detection: A Deep Learning-Based Approach. Journal of Universal Computer Science. 2024. vol. 30(2). pp. 262–286. DOI: 10.3897/jucs.104901.
20. Gibert D., Zizzo G., Le Q. Towards a Practical Defense Against Adversarial Attacks on Deep Learning-Based Malware Detectors via Randomized Smoothing. Lecture Notes in Computer Science. 2024. vol. 14399. pp. 683–699. DOI: 10.1007/978-3-031-54129-2_40.
21. Zhang P., Wu C., Wang Z. BINCODEX: A comprehensive and multi-level dataset for evaluating binary code similarity detection techniques. BenchCouncil Transactions on Benchmarks, Standards and Evaluations. 2024. vol. 4(2). DOI: 10.1016/j.tbench.2024.100163.
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24. Qian L., Cong L. Channel Features and API Frequency-Based Transformer Model for Malware Identification. Sensors. 2024. vol. 24(2). DOI: 10.3390/s24020580.
25. Surendran R., Uddin M.M., Thomas T., Pradeep G. Android Malware Detection Based on Informative Syscall Subsequences. IEEE Access. 2023. vol. 11. DOI: 10.1109/ACCESS.2024.3387475.
26. Kozak M., Jurecek M., Stamp M., Troia F.D. Creating valid adversarial examples of malware. Journal of Computer Virology and Hacking Techniques. 2024. vol. 20. pp. 607–621. DOI: 10.1007/s11416-024-00516-2.
27. Imran M., Appice A., Malerba D. Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection. Future Internet. 2024. vol. 16(5). DOI: 10.3390/fi16050168.
28. Saha S., Afroz S., Rahman A. H. MALIGN: Explainable static raw-byte based malware family classification using sequence alignment. Computers and Security. 2024. vol. 139. DOI: 10.1016/j.cose.2024.103714.
29. Li D., Cui S., Li Y., Xu J., Xiao F., Xu S. PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks. IEEE Transactions on Dependable and Secure Computing. 2024. vol. 21. no. 2. pp. 920–936. DOI: 10.1109/TDSC.2023.3265665.
30. Zhang F., Li K., Ren Z. Improving Adversarial Robustness of Ensemble Classifiers by Diversified Feature Selection and Stochastic Aggregation. Mathematics. 2024. vol. 12(6). DOI: 10.3390/math12060834.
31. Alzaidy S., Binsalleeh H. Adversarial Attacks with Defense Mechanisms on Convolutional Neural Networks and Recurrent Neural Networks for Malware Classification. Applied Sciences. 2024. vol. 14(4). DOI: 10.3390/app14041673.
32. Zhou K., Wang P., He B. Comparative Study: Mouth Brooding Fish (MBF) as a Novel Approach for Android Malware Detection. International Journal of Advanced Computer Science and Applications. 2024. vol. 15(5). DOI: 10.14569/IJACSA.2024.0150521.
33. Rakib H., Dhakal S.M. Obfuscated Malware Detection: Investigating Real-World Scenarios Through Memory Analysis. In 5th IEEE International Conference on Telecommunications and Photonics (ICTP 2023). 2023. DOI: 10.1109/ICTP60248.2023.10490701.
34. Carrier T., Victor P., Tekeoglu A., Lashkari A.H. Detecting Obfuscated Malware using Memory Feature Engineering. Proceedings of the 8th International Conference on Information Systems Security and Privacy (ICISSP). 2022. vol. 1. pp. 177–188. DOI: 10.5220/0010908200003120.
35. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and prediction (2nd ed.). Springer, 2009. 745 p.
36. Friedman J.H. Greedy function approximation: A gradient boosting machine. Annals of Statistics. 2001. vol. 29(5). pp. 1189–1232. DOI: 10.1214/aos/1013203451.
37. Ke G., Meng Q., Finley T., Wang T., Chen W., Ma W., Ye Q., Liu T. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017. pp. 31496–3157. DOI: 10.5555/3294996.3295074.
38. Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D., Brucher M., Perrot M., Duchesnay E. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 2011. vol. 12. pp. 2825–2830. DOI: 10.5555/1953048.2078195.
39. Chen T., Guestrin C. XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016. pp. 785–794. DOI: 10.1145/2939672.2939785.
40. Lundberg S.M., Lee S.-I. A Unified Approach to Interpreting Model Predictions. 2017. arXiv preprint arXiv:1705.07874. DOI: 10.48550/arXiv.1705.07874.
41. Ribeiro M.T., Singh S., Guestrin C. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016. pp. 1135–1144. DOI: 10.1145/2939672.293977.
42. Cevallos-Salas D., Grijalva F., Estrada-Jimenez J., Bentez D., Andrade R. Obfuscated Privacy Malware Classifiers Based on Memory Dumping Analysis. IEEE Access. 2024. vol. 12. pp. 17481–17498. DOI: 10.1109/ACCESS.2024.3358840.
43. Roy K.S., Ahmed T., Udas P.B. Karim M.E., Majumdar S. MalHyStack: A hybrid stacked ensemble learning framework with feature engineering schemes for obfuscated malware analysis. Intelligent Systems with Applications. 2023. vol. 20. DOI: 10.1016/j.iswa.2023.200283.
Опубликован
2025-01-20
Как цитировать
Имамвердиев, Я., Багиров, Э., & Икечукву, Д. Ч. (2025). Обнаружение обфусцированных вредоносных программ в Windows с помощью методов ансамблевого обучения. Информатика и автоматизация, 24(1), 99-124. https://doi.org/10.15622/ia.24.1.5
Раздел
Информационная безопасность
Copyright (c) Elshan Baghirov
Это произведение доступно по лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная.
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