Semi-supervised intrusion detection via online laplacian twin support vector machine

Mousavi, Arezoo; Ghidary, Saeed Shiry; Karimi, Zohre

doi:10.1109/spis.2015.7422328

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…It has been a critical component for network security. Researchers [38,51,94,98] applied TSVM for intrusion detection and results have shown that it achieves better accuracy than other intrusion detection algorithms.…”

Section: Applications Of Twin Support Vector Classificationmentioning

confidence: 99%

Comprehensive Review On Twin Support Vector Machines

Tanveer,

Rajani,

Rastogi

et al. 2021

Preprint

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Twin support vector machine (TSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TSVM and requires to solve two SVM kind problems. Although there has been good research progress on these techniques; there is limited literature on the comparison of different variants of TSVR. Thus, this review presents a rigorous analysis of recent research in TSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with we first introduce the basic theory of TSVM and then focus on the various improvements and applications of TSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects.

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Section: Applications Of Twin Support Vector Classificationmentioning

confidence: 99%

Comprehensive Review On Twin Support Vector Machines

Tanveer,

Rajani,

Rastogi

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, it can be regarded as a classification problem, leading to the development of several methods such as machine learning (ML), swarm, and evolutionary algorithms. Support vector machine (SVM), fuzzy rules, Naive Bayesian (NB), neural networks, and 𝑘NN are some of the classifiers employed for intrusion detection [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive k-nearest neighbor Classifier using Differential Evolution with Auto-Enhanced Population Diversity for Intrusion Detection

Karimi

Torabi

2022

Preprint

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Machine learning methods have attracted increasing interest in recent studies on intrusion detection. A classifier is applied to discriminate attacks from normal connections in these methods. 𝒌-nearest neighbor (𝒌NN) has been widely used in intrusion detection due to its simplicity and effectiveness. The classical 𝒌NN exploits Euclidean distance for identifying nearest neighbors, whereas how to compute the distance of data points is highly application-specific and plays a crucial role in the effectiveness of this classifier. In this paper, a novel 𝒌NN classifier is proposed that employs p-norm distance metric, the generalization of Euclidean distance, by learning p from data. The value of p in the proposed data-dependent metric is learned by the differential evolution algorithm exploiting auto-enhanced population diversity. The experimental results showed significant improvements in terms of F1 score and error rate compared to conventional kNN and Naive Bayesian classifiers on Kyoto2006+ and NSL-KDD. Furthermore, they verify the superiority of kNN classifier using the proposed data-dependent metric in terms of receiver operating characteristic curve and the corresponding area under the curve.

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