The Crossover Closure and Partial Match Detection

Esponda, Fernando; Forrest, Stephanie; Helman, Paul

doi:10.1007/978-3-540-45192-1_24

Cited by 8 publications

(6 citation statements)

References 19 publications

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“…Esponda et al (2003b) developed a formal framework for analyzing positive and negative detection under various matching criteria. They discussed in depth the idea of crossover closure (Balthrop et al, 2002;Esponda et al, 2003a), which obviously has some root in genetic algorithm. The problem addressed is to derive some means for inductively determining the underlying concept from a collection of instances.…”

Section: Analysis Of Detector Coveragementioning

confidence: 98%

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Revisiting Negative Selection Algorithms

Zhou

Dasgupta

2007

Evolutionary Computation

166

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This paper reviews the progress of negative selection algorithms, an anomaly/change detection approach in Artificial Immune Systems (AIS). Following its initial model, we try to identify the fundamental characteristics of this family of algorithms and summarize their diversities. There exist various elements in this method, including data representation, coverage estimate, affinity measure, and matching rules, which are discussed for different variations. The various negative selection algorithms are categorized by different criteria as well. The relationship and possible combinations with other AIS or other machine learning methods are discussed. Prospective development and applicability of negative selection algorithms and their influence on related areas are then speculated based on the discussion.

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Section: Analysis Of Detector Coveragementioning

confidence: 98%

“…One-class classification (Esponda et al, 2003a). Although the goal is to discriminate between two classes, samples from only one class (self or normal) are available to train the system.…”

Section: Introductionmentioning

confidence: 99%

Revisiting Negative Selection Algorithms

Zhou

Dasgupta

2007

Evolutionary Computation

166

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“…Since a r-contiguous detector can be decomposed into ℓ − r + 1 overlapping r-chunk detectors, r-chunk is considered as a simplification of the r contiguous n matching rule [21]. It has been showed in Ref.…”

Section: Detectorsmentioning

confidence: 99%

A Novel Negative Selection Algorithm with Optimal Worst-case Training Time Complexity for R-chunk Detectors

Trưởng¹

2020

IJST

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Objectives: To generate complete and non-redundant detector set with optimal worst-case time complexity. Methods: In this study, a novel exact matching and string-based Negative Selection Algorithm utilizing r-chunk detectors is proposed. Improved algorithms are tested on some data sets; the experiments' results are compared with recently published ones. Moreover, algorithms' complexities are also proved mathematically. Findings: For string-based Artificial Immune Systems, r-chunk detector is the most common detector type and their generation complexity is one of the important factors considered in the literature. We proposed optimal algorithms based on automata to present all detectors. Novelty/applications: The algorithm could generate the representation of complete and nonredundant detector set with optimal worst-case time complexity. To the best of our knowledge, the algorithm is the first one to possess such worst-case training time complexity.

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“…They are undesirable to the extent that they lead to false negatives if non-self samples are falling into them. Balthrop et al [28] and Esponda et al [86,87] pointed out that matching rules are one reason of inducing holes. For example, the r-contiguous bit matching rule induces either length-limited holes or crossover holes, while the r-chunks matching rule only induces crossover holes.…”

Section: Detector Generationmentioning

confidence: 99%

The use of computational intelligence in intrusion detection systems: A review

Banzhaf

2010

Applied Soft Computing

561

248

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Intrusion detection based upon computational intelligence is currently attracting considerable interest from the research community. Characteristics of computational intelligence (CI) systems, such as adaptation, fault tolerance, high computational speed and error resilience in the face of noisy information fit the requirements of building a good intrusion detection model. Here we want to provide an overview of the research progress in applying CI methods to the problem of intrusion detection. The scope of this review will be on core methods of CI, including artificial neural networks, fuzzy systems, evolutionary computation, artificial immune systems, swarm intelligence, and soft computing. The research contributions in each field are systematically summarized and compared, allowing us to clearly define existing research challenges, and to highlight promising new research directions. The findings of this review should provide useful insights into the current IDS literature and be a good source for anyone who is interested in the application of CI approaches to IDSs or related fields.

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The Crossover Closure and Partial Match Detection

Cited by 8 publications

References 19 publications

Revisiting Negative Selection Algorithms

Revisiting Negative Selection Algorithms

A Novel Negative Selection Algorithm with Optimal Worst-case Training Time Complexity for R-chunk Detectors

The use of computational intelligence in intrusion detection systems: A review

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