Data networks
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Intrusion Detection via Static Analysis
SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
Efficient randomized pattern-matching algorithms
IBM Journal of Research and Development - Mathematics and computing
Architecture for an Artificial Immune System
Evolutionary Computation
An antibody network inspired evolutionary framework for distributed object computing
Information Sciences: an International Journal
An immune inspired co-evolutionary affinity network for prefetching of distributed object
Journal of Parallel and Distributed Computing
A cooperative immunological approach for detecting network anomaly
Applied Soft Computing
Expert Systems with Applications: An International Journal
Structural design of the danger model immune algorithm
Information Sciences: an International Journal
R&D: Using danger to distribute defences
Infosecurity
A Systematic Survey of Self-Protecting Software Systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special Section on Best Papers from SEAMS 2012
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This paper represents a departure from the current paradigms of centralized attack defenses and introduces the idea of the Danger model to autonomic defense systems. In existing systems, such as anti-viruses (AV) or intrusion prevention systems (IPS), a central authority generates the defense mechanisms and deploys these to the systems in the field. While this strategy works fairly well in static systems, currently the trend is towards large and more dynamically configured systems. The future is likely to belong to ubiquitous systems where the number of devices and their diversity exceed the capacity to centrally administer them. Furthermore, ubiquitous systems will also contain many devices that are not connected all the time nor to all other devices equally. To address these issues, this paper looks at the Danger Model of computer immune systems and its application to attack defense to create a fully decentralized model. The main paradigms are co-stimulation using both evidence of an attack (knowledge-based or behavior-based) with evidence of real danger or damage. By combining these two detection models we are able to reduce the chance of an auto-immune reaction in the Active Defense Network.