DYNAMIC PACKET FILTERING USING MACHINE LEARNING METHODS
Keywords:
Machine learning, Unlike static filtering, new opportunitiesAbstract
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.
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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.
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