A Bayesian change point model for detecting SIP-based DDoS attacks (Journal Publication)

By | December 8, 2017

We have published the results of our DDoS attack detection project that we’ve been working on for 2 years on the Digital Signal Processing journal. You can reach our article here.


Session Initiation Protocol (SIP), as one the most common signaling mechanism for Voice Over Internet Protocol (VoIP) applications, is a popular target for the flooding-based Distributed Denial of Service (DDoS) attacks. In this paper, we propose a DDoS attack detection framework based on the Bayesian multiple change model, which can detect different types of flooding attacks. Additionally, we propose a probabilistic SIP network simulation system that provides a test environment for network security tools.

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