On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on Intrusion Detection Systems

Elhag, Salma; Fernández, Alberto; Bawakid, Abdullah; Alshomrani, Saleh; Herrera, Francisco

doi:10.1016/j.eswa.2014.08.002

Cited by 179 publications

(77 citation statements)

References 52 publications

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“…According to the authors, the use of fuzzy sets, and especially linguistic labels, enables a smoother borderline between the concepts, and allows a higher interpretability of the rule set which provides an advantage to the system .Results of the experiments carried on KDDCUP'99 have shown that proposed approach has the best tradeoff among all performance measures. The authors have also stated, the use of fuzzy logic as a tool allows addressing the vague division that exists between normal and anomalous accesses properly [29].…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

“…Another work that uses Genetic fuzzy systems is the work by Elhag et al, (2015) which describes the use of Genetic Fuzzy Systems within a pair wise learning framework. According to the authors, the use of fuzzy sets, and especially linguistic labels, enables a smoother borderline between the concepts, and allows a higher interpretability of the rule set which provides an advantage to the system .Results of the experiments carried on KDDCUP'99 have shown that proposed approach has the best tradeoff among all performance measures.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

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A Review of Soft Computing Solutions to Intrusion in Computer Network

Shukla¹,

Sharma²,

Hota³

2016

IJCA

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With the wide adoption of internet, there lies threat to sensitive information being shared on the network. An intrusion can be defined as an act or a number of acts in a sequence that either cause a compromise or intend to compromise the information. These intrusions need somehow to be detected so that the harm caused by them may be prevented. A system that keeps looking for activities in a computer or a network to detect intrusions is called an Intrusion Detection System (IDS).Many novel methods of intrusion detection involve the use of Soft Computing tools. Soft Computing (SC) is a collection of methods used for developing intelligent systems for the problems for which conventional techniques have not given low cost or complete solutions. Artificial Neural Networks (ANN), Fuzzy Logic theory (FL) and Genetic Algorithm (GA) represent the most common tools of soft computing. The capability of soft computing tools to tolerate imprecision, partial truth, uncertainty and ability to provide low solution costs to real world problems and computationally intelligent problems are the obvious reasons which has made this approach widely accepted in field of Intrusion DetectionIn this research work, we have collected about thirty papers to study how different soft computing tools and techniques can be utilized to develop efficient and robust Intrusion detection Systems.

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Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

A Review of Soft Computing Solutions to Intrusion in Computer Network

Shukla¹,

Sharma²,

Hota³

2016

IJCA

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“…The use of decomposition strategies in multiclassification has shown to be of great interest in the research community [2,4], including FRBCS [21,22,20]. The main idea for this learning scheme is to address a multiple classes problem by means of binary classifiers, following a divide and conquer paradigm.…”

Section: Decomposition Strategies: One-vs-onementioning

confidence: 99%

Improving Pairwise Learning Classification in Fuzzy Rule Based Classification Systems Using Dynamic Classifier Selection

Fernández¹,

Galar²,

Sanz³

et al. 2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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Classification based on the One-vs-One decomposition strategy has shown a high quality for addressing those problems with multiple classes, even if the learning model enables the discrimination among several concepts. The main phase of the pairwise learning is the decision process, where the outputs of the binary classifiers are combined to give a single output. Recently, it has been shown that standard decision techniques do not take into account the influence of the non-competent classifiers, i.e. those that were not trained using the class of the query example, and this can deteriorate the performance of the model. In accordance with the former, a "Dynamic Classifier Selection" for the Onevs-One approach was proposed to alleviate this issue. It basically consists of finding those classifiers whose outputs are closest to the input example, and thus remove those ones which are not related with it. In this work, we want to analyse the goodness for the former approach using a fuzzy-type baseline classifier. Experimental results show that there is in fact a significant leap in the global performance when this model is applied, both versus the standard fuzzy rule based classification system, and the One-vs-One learning approach.

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“…In term of cyber security, those methods are complement to the signature approaches. It should be noted that the signature based IDS performs better in detecting the well known patterns of intrusion, while the anomaly based ones suits for the unknown patterns [9].…”

Section: Introductionmentioning

confidence: 99%

A performance study of anomaly detection using entropy method

Waskita

Suhartanto

Handoko

2016

2016 International Conference on Computer, Control, Informatics and Its Applications (IC3INA)

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Abstract-An experiment to study the entropy method for an anomaly detection system has been performed. The study has been conducted using real data generated from the distributed sensor networks at the Intel Berkeley Research Laboratory. The experimental results were compared with the elliptical method and has been analyzed in two dimensional data sets acquired from temperature and humidity sensors across 52 micro controllers. Using the binary classification to determine the upper and lower boundaries for each series of sensors, it has been shown that the entropy method are able to detect more number of out ranging sensor nodes than the elliptical methods. It can be argued that the better result was mainly due to the lack of elliptical approach which is requiring certain correlation between two sensor series, while in the entropy approach each sensor series is treated independently. This is very important in the current case where both sensor series are not correlated each other.

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On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on Intrusion Detection Systems

Cited by 179 publications

References 52 publications

A Review of Soft Computing Solutions to Intrusion in Computer Network

A Review of Soft Computing Solutions to Intrusion in Computer Network

Improving Pairwise Learning Classification in Fuzzy Rule Based Classification Systems Using Dynamic Classifier Selection

A performance study of anomaly detection using entropy method

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