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Title: Effectiveness analysis of public rule sets used in snort intrusion detection system
Other Titles: Snort saldiri tespit sisteminde kullanilan açik kural setlerinin etkinlik analizi
Authors: Gündoğdu, I.
Selçuk, Ali Aydın
Özarslan, S.
Keywords: Intrusion Detection System
Public Rule Sets
Open systems
Signal processing
Attack detection
Attack traffic
Different attacks
Effectiveness analysis
Intrusion Detection Systems
Open sources
Rule set
Web application attacks
Intrusion detection
Issue Date: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: Snort is one of the most used open source intrusion detection systems today. It is also supported by a large number of open source rulesets. The purpose of this study is to test the effectiveness of the public rule sets developed for the Snort intrusion detection system against different types of attack traffic. For this purpose, by configuring the Snort attack detection system with different rule sets, experiments have been conducted to measure whether each rule set prevents different attack types such as CVE-referenced vulnerability exploitation attacks, web application attacks and malware traffics. During the experiments, the rule sets were tested separately as well as tests in which all the rule sets were used together. As a result of the experiments, it was observed that the most effective rule set when used alone was the Talos rule set, and the highest efficiency was achieved when all the rule sets were used together. © 2021 IEEE.
Description: 29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 -- 9 June 2021 through 11 June 2021 -- 170536
ISBN: 9781665436496
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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