Data Mining Texnologiyalarining Kiberxavfsizlikdagi Qo‘llanilishi
Kalit so'zlar:
Data Mining, kiberxavfsizlik, mashinani o‘rganish, anomaliya aniqlash, kiberhujumlar, Random Forest, Neural Networks, intrusion detectionAnnotatsiya
Ushbu maqolada Data Mining texnologiyalarining zamonaviy kiberxavfsizlik tizimlarida qo‘llanilishi tadqiq etilgan. Tadqiqot davomida NSL-KDD va CICIDS2017 datasetlari asosida Random Forest, Neural Networks, SVM, Decision Trees va anomaliya aniqlash algoritmlari tahlil qilindi. Natijalar shuni ko‘rsatdiki, Random Forest algoritmi 98.7% aniqlik bilan eng yuqori samaradorlikka ega, anomaliya aniqlash usullari esa noma'lum hujumlarni 94.2% aniqlik bilan aniqlaydi. Gibrid yondashuvlar - klassifikatsiya va anomaliya aniqlashni birlashtirish - kiberxavfsizlik tizimlarining samaradorligini sezilarli darajada oshiradi. Tadqiqot amaliy sinovlarda 47 ta real hujumni muvaffaqiyatli aniqladi va 2.1% false positive ko‘rsatkichiga erishdi
Библиографические ссылки
Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176.
2. Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. 2010 IEEE Symposium on Security and Privacy, 305-316.
3. Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1-58.
4. Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009). A detailed analysis of the KDD CUP 99 data set. IEEE Symposium on Computational Intelligence for Security and Defense Applications, 1-6.
5. Sharafaldin, I., Lashkari, A. H., & Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. 4th International Conference on Information Systems Security and Privacy (ICISSP), 108-116.
6. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
7. Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008). Isolation forest. 2008 Eighth IEEE International Conference on Data Mining, 413-422.
8. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.
9. Shiravi, A., Shiravi, H., Tavallaee, M., & Ghorbani, A. A. (2012). Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Computers & Security, 31(3), 357-374.
10. Han, J., Pei, J., & Kamber, M. (2011). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann Publishers.
11. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases, 487-499.
12. Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504-507.
13. Debar, H., Dacier, M., & Wespi, A. (1999). Towards a taxonomy of intrusion-detection systems. Computer Networks, 31(8), 805-822.
14. Khraisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019). Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity, 2(1), 1-22.
15. Ring, M., Wunderlich, S., Scheuring, D., Landes, D., & Hotho, A. (2019). A survey of network-based intrusion detection data sets. Computers & Security, 86, 147-167.
