INTELLIGENT ALGORITHMS FOR DETECTING NETWORK PORT ATTACKS IN CYBERSECURITY
DOI:
https://doi.org/10.5281/zenodo.19876929Keywords:
cybersecurity, port scanning, anomaly detection, Shannon entropy, Random Forest, hybrid model, network traffic analysis.Abstract
This study is devoted to the problem of intelligent detection of network port scanning attacks, which is an important aspect of cybersecurity. The paper analyzes the limitations of traditional signature-based systems, particularly their inability to effectively detect low-intensity and stealth attacks. A hybrid model combining statistical methods (Shannon entropy) and machine learning (Random Forest) is proposed. The proposed approach analyzes network traffic in real time, ensuring high efficiency and a low error rate. The practical part of the study is implemented in the Python environment and validated using the CIC-IDS2017 dataset.
References
Shannon, C. E. (1948). A Mathematical Theory of Communication.
Bell System Technical Journal, 27(3), 379-423. (Entropiya asoschisi).
Lashkari, A. H., et al. (2017). Toward Generating a New Dataset for Intrusion Detection System Framework. Proceedings of the 15th Annual Conference on Privacy, Security and Trust (PST), IEEE, 1-10. (CIC-IDS2017 dataseti mualliflari).
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. (Algoritm asoschisi).
Scarfone, K., & Mell, P. (2018). Guide to Intrusion Detection and Prevention Systems (IDPS). NIST Special Publication 800-94, National Institute of Standards and Technology.
Tavallaee, M., et al. (2009). A Detailed Analysis of the KDD CUP 99 Data Set. IEEE Symposium on Computational Intelligence for Security and Defense Applications, 1-6.
Ahmad, I., et al. (2021). Efficient Ensemble Machine Learning for Port Scanning Detection. IEEE Access, 9, 112345-112360.
Yusupov, Sh. R. (2022). Kiberxavfsizlikda anomaliyalarni aniqlashning intellektual usullari. O‘zbekiston Milliy universiteti xabarlari, 2(1), 145-152.
Pedregosa, F., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
V. Paxson. (1999). Bro: A System for Detecting Network Intruders in Real-Time. Computer Networks, 31(23-24), 2435-2463.
Zheng, D., et al. (2020). Port Scan Detection Based on Evidence Theory and Entropy. Future Internet, 12(10), 164
