Self-organizing maps
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
CDIS: Towards a Computer Immune System for Detecting Network Intrusions
RAID '00 Proceedings of the 4th International Symposium on Recent Advances in Intrusion Detection
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
An Immunological Approach to Change Detection: Algorithms, Analysis and Implications
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
Anomaly Detection Using Real-Valued Negative Selection
Genetic Programming and Evolvable Machines
Detecting Flaws and Intruders with Visual Data Analysis
IEEE Computer Graphics and Applications
A Visual Approach for Monitoring Logs
LISA '98 Proceedings of the 12th USENIX conference on System administration
Architecture for an Artificial Immune System
Evolutionary Computation
An artificial immune system architecture for computer securityapplications
IEEE Transactions on Evolutionary Computation
Revisiting Negative Selection Algorithms
Evolutionary Computation
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
| Hi-index | 0.00 |
An immune inspired model that can detect anomalies, even when trained only with normal samples, and can learn from encounters with new anomalies is presented. The model combines a negative selection algorithm and a self-organizing map (SOM) in an immune inspired architecture. The proposed system is able to produce a visual representation of the self/non-self feature space, thanks to the topological 2-dimensional map produced by the SOM. Some experiments were performed on classification data; the results are presented and discussed.