IEEE Transactions on Software Engineering - Special issue on computer security and privacy
Temporal sequence learning and data reduction for anomaly detection
ACM Transactions on Information and System Security (TISSEC)
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Anomaly Detection Using Real-Valued Negative Selection
Genetic Programming and Evolvable Machines
Architecture for an Artificial Immune System
Evolutionary Computation
How Do We Evaluate Artificial Immune Systems?
Evolutionary Computation
Machine Learning for Computer Security
The Journal of Machine Learning Research
Revisiting Negative Selection Algorithms
Evolutionary Computation
An overview of anomaly detection techniques: Existing solutions and latest technological trends
Computer Networks: The International Journal of Computer and Telecommunications Networking
A hybrid artificial immune system and Self Organising Map for network intrusion detection
Information Sciences: an International Journal
A Data Mining Methodology for Anomaly Detection in Network Data
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
MILA: multilevel immune learning algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
The effect of binary matching rules in negative selection
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
On permutation masks in hamming negative selection
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
An immunity-based technique to characterize intrusions in computernetworks
IEEE Transactions on Evolutionary Computation
A formal framework for positive and negative detection schemes
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 12.05 |
In real-valued negative selection algorithm, the variability of self sample would result in the holes on the boundary between the self and non-self region and the deceiving anomalies hidden in the self region. This paper analyzes the reason for the difficulty in handling these problems by traditional evolved detectors, and then proposes a method of evolving boundary detectors to solve them. This method uses an improved detector generation algorithm based on evolutionary search to generate boundary detectors. The boundary detectors constructed by an aggressive interpretation are allowed to cover a part of self region. The aggressiveness controlled by boundary threshold can convert some volume of self sample into the fitness of boundary detector. This makes them enable to eliminate the holes on the boundary and have an opportunity to detect the deceiving anomalies hidden in the self region. Experiments are carried out using both 2-dimensional dataset and real world dataset. The former was designed to demonstrate intuitively that boundary detectors can cover the holes on the boundary, while the latter was to show that boundary detectors can detect the deceiving anomalies.