DYNAMIC PACKET FILTERING USING MACHINE LEARNING METHODS
Abstract
Machine learning methods offer new opportunities for dynamic packet filtering in network security, allowing for more efficient and intelligent decision-making in filtering processes. Traditional packet filtering techniques often rely on static rules, which may not adequately respond to evolving threats or adapt to changing network conditions. Machine learning approaches enable the filtering system to learn from traffic patterns, identify anomalies, and improve filtering accuracy over time.
Keywords
Machine learning, Unlike static filtering, new opportunitiesHow to Cite
References
Gulomov Sh.R. Types of malicious traffic in the network and their detection. Multidisciplinary Scientific Journal. December, Issue 24 | 2023, pp. 424-432.
SN Tashev, AG Ganiev The Role of “Imagination” in the Process of “Creative Thinking” Developing Students' “Imagination” and “Creative Thinking” Skills in Teaching Physics. Annals of the Romanian Society for Cell Biology, 2021/3/6, pp. 633-642.
SN Tashev THE ROLE OF “IMAGINATION” IN THE PROCESS OF “CREATIVE THINKING”, DEVELOPING STUDENTS’ “IMAGINATION” AND “CREATIVE THINKING” SKILLS IN TEACHING PHYSICS PSYCHOLOGY AND EDUCATION, pp. 3569-3575.
Y.B. Karamatovich, T.S. Norboboevich, N.I. Ibrohimovich. Verification of the pocket filtering based on method of verification on the model. 2019 International Conference on Information Science and Communications Technologies (ICISCT).
J. Ning et al., “Pine: Enabling privacy-preserving deep packet inspection on TLS with rule-hiding and fast connection establishment,” in Proc. Eur. Symp. Res. Comput. Secur., 2020, pp. 3–22.
License
Copyright (c) 2024 Sarvar Norboboyevich Tashev
This work is licensed under a Creative Commons Attribution 4.0 International License.
The content published on the International Scientific and Current Research Conferences platform, including conference papers, abstracts, and presentations, is made available under an open-access model. Users are free to access, share, and distribute this content, provided that proper attribution is given to the original authors and the source.
