The T-detectors maturation algorithm based on match range model
Proceedings of the 2005 ACM symposium on Applied computing
A Novel Fuzzy Anomaly Detection Algorithm Based on Artificial Immune System
HPCASIA '05 Proceedings of the Eighth International Conference on High-Performance Computing in Asia-Pacific Region
Integrated platform of artificial immune system for anomaly detection
AIKED'05 Proceedings of the 4th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering Data Bases
T-detector maturation algorithm with overlap rate
WSEAS Transactions on Computers
Storage-Based Intrusion Detection Using Artificial Immune Technique
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
A model of collaborative artificial immune system
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 3
Theoretical basis of novelty detection in time series using negative selection algorithms
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Dynamic negative selection algorithm based on match range model
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Fuzzy anomaly detection system for IPv6 (FADS6): an immune-inspired algorithm with hash function
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Static clonal selection algorithm based on match range model
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
The t-detectors maturation algorithm based on genetic algorithm
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Adaptive hybrid immune detector maturation algorithm
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Self-regulating method for model library based artificial immune systems
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Expert Systems with Applications: An International Journal
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The main goal of this research is to examine and to improve the anomaly detection function of artificial immune systems, specifically the negative selection algorithm and other self/non-self recognition techniques. This research investigates different representation schemes for the negative selection and proposes new detector generation algorithms suitable for such representations. Accordingly, different representations are explored: hyper-rectangles (which can be interpreted as rules), fuzzy rules, and hyper-spheres. Four different detector generation algorithms are proposed: Negative Selection with Detection Rules (NSDR, an evolutionary algorithm to generate hypercube detectors), Negative Selection with Fuzzy Detection Rules (NSFDR, an evolutionary algorithm to generate fuzzy-rule detectors), Real-valued Negative Selection (RNS, a heuristic algorithm to generate hyper-spherical detectors), and Randomized Real-valued Negative Selection (RRNS, an algorithm for generating hyper-spherical detectors based on Monte Carlo methods). Also, a hybrid immune learning algorithm, which combines RNS (or RRNS) and classification algorithms is developed. This algorithm allows the application of a supervised learning technique even when samples from only one class (normal) are available. Different experiments are performed with synthetic and real world data from different sources. The experimental results show that the proposed representations along with the proposed algorithms provide some advantages over the binary negative selection algorithm. The most relevant advantages include improved scalability, more expressiveness that allows the extraction of high-level domain knowledge, non-crisp distinction between normal and abnormal, and better performance in anomaly detection.