Algorithms for clustering data
Algorithms for clustering data
Communications of the ACM
Coverage and Generalization in an Artificial Immune System
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Two state-based approaches to program-based anomaly detection
ACSAC '00 Proceedings of the 16th Annual Computer Security Applications Conference
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Intrusion detection using sequences of system calls
Journal of Computer Security
Theoretical advances in artificial immune systems
Theoretical Computer Science
An Empirical Study of Self/Non-self Discrimination in Binary Data with a Kernel Estimator
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Comparative Evaluation of Anomaly Detection Techniques for Sequence Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
ACM Computing Surveys (CSUR)
Efficient Algorithms for String-Based Negative Selection
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Negative selection algorithms without generating detectors
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Negative selection algorithms on strings with efficient training and linear-time classification
Theoretical Computer Science
An immunological approach to change detection: algorithms, analysis and implications
SP'96 Proceedings of the 1996 IEEE conference on Security and privacy
A sense of self for Unix processes
SP'96 Proceedings of the 1996 IEEE conference on Security and privacy
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The negative selection algorithm is one of the oldest immune-inspired classification algorithms and was originally intended for anomaly detection tasks in computer security. After initial enthusiasm, performance problems with the algorithm lead many researchers to conclude that negative selection is not a competitive anomaly detection technique. However, in recent years, theoretical work has lead to substantially more efficient negative selection algorithms. Here, we report the results of the first evaluation of negative selection with r-chunk and r-contiguous detectors that employs these novel algorithms. On a collection of 14 datasets from real-world sources, we compare negative selection with r-chunk and r-contiguous detectors against techniques based on kernels, finite state automata, and n-gram frequencies, and find that negative selection performs competitively, yielding a slightly better average performance than all other techniques investigated. Because this study represents, to our knowledge, the most comprehensive one of string-based negative selection to date, the widely held view that negative selection is not a competitive anomaly detection technique may be inaccurate.