NETWORK PACKET ANALYZING METHODS USED BY INTRUSION DETECTION SYSTEMS

Article Sidebar

PDF
Issue: Vol. 2 No. 39 : Science and Innovation
Section: Articles
Published:

Main Article Content

TUIT named after Muhammad al-Khwarizmi
info@in-academy.uz

Abstract:

Intrusion Detection Systems (IDS) are vital tools for cybersecurity, monitoring network traffic to detect and mitigate attacks. This article explores the mechanisms of network packet analyzing used by IDS, providing a detailed examination of methods, technologies, and comparisons. The study utilizes recent literature to review advancements, describe IDS and their operations, and correlate packet analysis techniques with specific IDS types.

Keywords:

Article Details

How to Cite:

Bozorov, S. (2024). NETWORK PACKET ANALYZING METHODS USED BY INTRUSION DETECTION SYSTEMS. Science and Innovation, 2(39), 81–88. Retrieved from https://in-academy.uz/index.php/si/article/view/42556

References:

I. Sumaiya Thaseen, B. Poorva and P. S. Ushasree, "Network Intrusion Detection using Machine Learning Techniques," 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India, 2020, pp. 1-7, doi: 10.1109/ic-ETITE47903.2020.148.

Park, S., Park, H., & Choi, Y. (2020). RNN-based Prediction for Network Intrusion Detection. 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 572-574. https://doi.org/10.1109/ICAIIC48513.2020.9065249.

Wang, J., Sun, L., & Jia, L. (2022). Research on Computer Network Intrusion Detection Technology. 2022 International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI), 330-333. https://doi.org/10.1109/ICDACAI57211.2022.00071.

T. Su, H. Sun, J. Zhu, S. Wang and Y. Li, "BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset," in IEEE Access, vol. 8, pp. 29575-29585, 2020, doi: 10.1109/ACCESS.2020.2972627.

E. Papadogiannaki, G. Tsirantonakis and S. Ioannidis, "Network Intrusion Detection in Encrypted Traffic," 2022 IEEE Conference on Dependable and Secure Computing (DSC), Edinburgh, United Kingdom, 2022, pp. 1-8, doi: 10.1109/DSC54232.2022.9888942.

Adewole, L. B., Adeyeye, C. R., Adetunmbi, A. O., Ayogu, B. A., & Folorunsho, O. (2020). Abstracting Packet Header Information for Intrusion Detection in High-Speed Networks. Journal of Engineering and Technology. DOI: 10.46792/FUOYEJET.V5I2.541

E. T. Toivo, A. W. Kambrude and A. M. Gamundani, "Packet Forensic Analysis in Intrusion Detection Systems," 2021 3rd International Multidisciplinary Information Technology and Engineering Conference (IMITEC), Windhoek, Namibia, 2021, pp. 1-4, doi: 10.1109/IMITEC52926.2021.9714593.

Y. A. Farrukh, I. Khan, S. Wali, D. Bierbrauer, J. A. Pavlik and N. D. Bastian, "Payload-Byte: A Tool for Extracting and Labeling Packet Capture Files of Modern Network Intrusion Detection Datasets," 2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), Vancouver, WA, USA, 2022, pp. 58-67, doi: 10.1109/BDCAT56447.2022.00015.

M. Hassan, M. E. Haque, M. E. Tozal, V. Raghavan and R. Agrawal, "Intrusion Detection Using Payload Embeddings," in IEEE Access, vol. 10, pp. 4015-4030, 2022, doi: 10.1109/ACCESS.2021.3139835.

T. Kim and W. Pak, "Early Detection of Network Intrusions Using a GAN-Based One-Class Classifier," in IEEE Access, vol. 10, pp. 119357-119367, 2022, doi: 10.1109/ACCESS.2022.3221400.