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.

Abstract:

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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