The 1999 DARPA off-line intrusion detection evaluation
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
An Intelligent Decision Support System for Intrusion Detection and Response
MMM-ACNS '01 Proceedings of the International Workshop on Information Assurance in Computer Networks: Methods, Models, and Architectures for Network Security
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
A study of artificial immune systems applied to anomaly detection
A study of artificial immune systems applied to anomaly detection
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More and more intrusion detection systems were developed, but most of these systems have very poor accuracy. To overcome this problem, a self-adaptive anomaly detection system was developed using fuzzy detection anomaly algorithm with negative selection of biology. The algorithm improves the accuracy of the detection method and produces a novel method to measure the deviation from the normal that does not need a discrete division of the non-self space. The proposed anomaly detection model is designed as flexible, extendible, and adaptable in order to meet the needs and preferences of network administrators and can be also supplied for IPv6 environment. Different experiments are performed with MIT-DARAP 1999 dataset[1] and real word data from different sources. The experimental results show that the proposed algorithms provide some advantages over other algorithms.