Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm

Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system, providing the initial detection of pathogenic invaders. Research into this family of cells has revealed that they perform information fusion which directs immune responses. We have derived a Dendritic Cell Algorithm based on the functionality of these cells, by modelling the biological signals and differentiation pathways to build a control mechanism for an artificial immune system. We present algorithmic details in addition to experimental results, when the algorithm was applied to anomaly detection for the detection of port scans. The results show the Dendritic Cell Algorithm is sucessful at detecting port scans.Comment: 21 pages, 17 figures, Information Fusio

Cyber-Physical Security Strategies

Cyber-physical security describes the protection of systems with close relationships between computational functions and physical ones and addresses the issue of vulnerability to attack through both cyber and physical avenues. This describes systems in a wide variety of functions, many crucial to the function of modern society, making their security of paramount importance. The development of secure system design and attack detection strategies for each potential avenue of attack is needed to combat malicious attacks. This thesis will provide an overview of the approaches to securing different aspect of cyber-physical systems. The cyber element can be designed to better prevent unauthorized entry and to be more robust to attack while its use is evaluated for signs of ongoing intrusion. Nodes in sensor networks can be evaluated by their claims to determine the likelihood of their honesty. Control systems can be designed to be robust in cases of the failure of one component and to detect signal insertion or replay attack. Through the application of these strategies, the safety and continued function of cyber-physical systems can be improved

BIOLOGICAL INSPIRED INTRUSION PREVENTION AND SELF-HEALING SYSTEM FOR CRITICAL SERVICES NETWORK

With the explosive development of the critical services network systems and Internet, the need for networks security systems have become even critical with the enlargement of information technology in everyday life. Intrusion Prevention System (IPS) provides an in-line mechanism focus on identifying and blocking malicious network activity in real time. This thesis presents new intrusion prevention and self-healing system (SH) for critical services network security. The design features of the proposed system are inspired by the human immune system, integrated with pattern recognition nonlinear classification algorithm and machine learning. Firstly, the current intrusions preventions systems, biological innate and adaptive immune systems, autonomic computing and self-healing mechanisms are studied and analyzed. The importance of intrusion prevention system recommends that artificial immune systems (AIS) should incorporate abstraction models from innate, adaptive immune system, pattern recognition, machine learning and self-healing mechanisms to present autonomous IPS system with fast and high accurate detection and prevention performance and survivability for critical services network system. Secondly, specification language, system design, mathematical and computational models for IPS and SH system are established, which are based upon nonlinear classification, prevention predictability trust, analysis, self-adaptation and self-healing algorithms. Finally, the validation of the system carried out by simulation tests, measuring, benchmarking and comparative studies. New benchmarking metrics for detection capabilities, prevention predictability trust and self-healing reliability are introduced as contributions for the IPS and SH system measuring and validation. Using the software system, design theories, AIS features, new nonlinear classification algorithm, and self-healing system show how the use of presented systems can ensure safety for critical services networks and heal the damage caused by intrusion. This autonomous system improves the performance of the current intrusion prevention system and carries on system continuity by using self-healing mechanism