IEEE Computer Society, 2002.SYN floods are back in vogue. of the IEEE International conference INFOCOMM 2002, pages 1530-1539. of IEEE Global Telecommunications Conference, pages 2050-2054, Dallas TX, 2004. Application of anomaly detection algorithms for detecting SYN Fooding attacks. International Journal of Unconventional Computing, 1:221-254, 2005. An artificial immune system for misbehavior detection in mobile ad-hoc networks with virtual thymus, clustering, danger signal and memory detectors. IEEE Transactions on Neural Networks, Special Issue on Adaptive Learning Systems in Communication Networks, 16(5):1076-1087, 2005. An artificial immune system approach with secondary response for misbehavior detection in mobile ad-hoc networks. of the Third International IFIP-TC6 Networking Conference (Networking 2004), volume 3042 of LNCS, pages 771-782, Athens, Greece, 2004. Detecting distributed denial of service attacks using source IP address monitoring. Detecting distributed denial-of-service by analyzing TCP SYN packets statistically.

Tolerance, danger and the extended family. Intel Technology Journal, 10(4), November 2006.available at. HP autonomic research initiatives (Adaptive Enterprise).

Autonomic computing: IBM's perspective on the state of information technology. and S.L., editors, Design Principles for the Immune System and other Distributed Autonomous Systems, pages 3-27. An interpretative introduction to the immune system.

Security in an autonomic computing environment. EU Funded project: Component-ware for Autonomic Situation-aware Communications, and Dynamically Adaptable Services. of 1st International Conference on Artificial Immune Systems (ICARIS), pages 41-148, University of Kent at Canterbury, 2002. The danger theory and its application to artificial immune systems. of 2nd Internation Conference on Artificial Immune Systems (ICARIS-03), volume 2787 of LNCS, pages 147-155. Danger theory: The link between AIS and IDS. We assert that the work will be useful in designing the security of autonomic networks. To empirically validate our proposal, we conduct experiments in a simulated environment and the results are encouraging. The proposed scheme is able to detect SYN flood attack in its early stage, thereby enabling to control the damage. We, therefore, propose a danger theory based approach that can detect DoS attack in an automatic manner. Obviously, such facility is not assumed in autonomic networks. Traditionally, the detection of flooding attacks is achieved by a network-based intrusion detection system (IDS), mainly relying on the statistical characteristics of network data with fine tuning from a human administrator by monitoring the traffic continuously.

Denial of service (DoS) attack, in particular flooding attack, is one of the most frequent and devastating attacks on networks. In such an environment, security of the system is very challenging as there is no dedicated authority to monitor malicious activities and each entity, the computing device, has to monitor itself. These self-* properties ensure functioning of the network without or very minimum human intervention. self-configuring, self-awareness, self-optimization, self-healing and self-protection, collectively denoted as self-*) entities. An autonomous communication network consists of self-managed (i.e. In the context of autonomic environment, we present a simple yet, effective Danger Theory based method to detect TCP SYN Flooding attack.

Syn attack