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2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications
Detecting and Tracing DDoS Attacks by Intelligent Decision Prototype
March 17-March 21
ISBN: 978-0-7695-3113-7
Over the last couple of months a large number of Distributed Denial of Service (DDoS) attacks have occurred across the world, especially targeting those who provide web services. IP traceback, a counter measure against DDoS, is the ability to trace IP packets back to the true source/s of the attack. In this paper, an IP traceback scheme using a machine learning technique called Intelligent Decision Prototype (IDP), is proposed. IDP can be used on both Probabilistic Packet Marking (PPM) and Deterministic Packet Marking (DPM) traceback schemes to identify DDoS attacks. This will greatly reduce the packets that are marked and in effect make the system more efficient and effective at tracing the source of an attack compared with other methods. IDP can be applied to many security systems such as Data Mining, Forensic Analysis, Intrusion Detection Systems (IDS) and DDoS defense systems.
Index Terms:
IP Traceback, Machine Learning, Decision trees, Distributed Denial of Service, Intelligent Decision Prototype
Citation:
Ashley Chonka, Wanlei Zhou, Jaipal Singh, Yang Xiang, "Detecting and Tracing DDoS Attacks by Intelligent Decision Prototype," percom, pp.578-583, 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications, 2008
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