Akademik Veri Yönetim Sistemi
16th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022, Biarritz, Fransa, 8 - 12 Ağustos 2022, (Tam Metin Bildiri)
© 2022 IEEE.In recent years, with the development of IP-based systems, circuit-switched systems have rapidly started to be replaced by packet-switched systems in communication infrastructures and operators have started to prefer VoIP systems more due to their advantages such as cost and resource efficiency. However, since VoIP (Voice over Internet Protocol) systems are equally open to all threats to which IP-based systems are open, researchers have proposed different methods for obtaining strong security solutions. Recently, rule-based systems have been replaced by machine learning-based systems in many areas and different machine learning-based solutions have been suggested for VoIP security.In this study, a machine learning-based solution was proposed for detecting SIP flooding attacks within the scope of DoS (Denial of Service) attacks which is one of the current threats to VoIP infrastructure. For this purpose, a test environment was created on a previously developed simulation infrastructure, primarily normal traffic and attack traffic were generated, and then the effectiveness of certain machine learning methods in the classification of traffic was tested with the labeled data obtained.When the results are evaluated with the parameters based on the working conditions, it is observed that the related methods can produce meaningful results.