A Rule Based Approach for Attribute Selection and Intrusion Detection in Wireless Sensor Networks

Anand, K.; Ganapathy, Sannasi; Kulothungan, K.; Yogesh, P.; Kannan, A.

doi:10.1016/j.proeng.2012.06.202

Cited by 22 publications

(9 citation statements)

References 3 publications

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“…In [28], Anand et al proposed a feature selection algorithm, in which the IG measure is computed for the training set. After creating the ranking, the feature with the higher score is selected.…”

Section: Related Workmentioning

confidence: 99%

A multi-measure feature selection algorithm for efficacious intrusion detection

Herrera-Semenets¹,

Bustio-Martínez²,

Hernández-León³

et al. 2021

Knowledge-Based Systems

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Section: Related Workmentioning

confidence: 99%

A multi-measure feature selection algorithm for efficacious intrusion detection

Herrera-Semenets¹,

Bustio-Martínez²,

Hernández-León³

et al. 2021

Knowledge-Based Systems

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“…Sindhu et al (2012) proposed a GA-based feature selection algorithm which is based on wrapper method for improving the intrusion detection accuracy. Anand et al (2012) proposed a new rule-based attribute selection algorithm for intrusion detection in wireless networks Ganapathy et al (2013) proposed an intelligent agent-based attribute selection algorithm for improving the intrusion detection accuracy. Ganapathy et al (2016) proposed an intelligent conditional random field-based feature selection algorithm which is useful for selecting optimal number of features for improving the intrusion detection accuracy in layered approach.…”

Section: Work On Ids Using Data Pre-processingmentioning

confidence: 99%

Intelligent Intrusion Detection Techniques for Secure Communications in Wireless Networks: A Survey

Prabha

Jeyanthi

2018

IJAIP

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“…In [12], a rule based feature selection algorithm is proposed to remove redundant attributes and to select sensitive feature set that is valuable for intrusive analysis engine in wireless sensor networks. The focus was only denial of service attacks.…”

Section: Literature Reviewmentioning

confidence: 99%

Feature Selection Using Particle Swarm Optimization in Intrusion Detection

Ahmad¹

2015

International Journal of Distributed Sensor Networks

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The prevention of intrusion in networks is decisive and an intrusion detection system is extremely desirable with potent intrusion detection mechanism. Excessive work is done on intrusion detection systems but still these are not powerful due to high number of false alarms. One of the leading causes of false alarms is due to the usage of a raw dataset that contains redundancy. To resolve this issue, feature selection is necessary which can improve intrusion detection performance. Latterly, principal component analysis (PCA) has been used for feature reduction and subset selection in which features are primarily projected into a principal space and then features are elected based on their eigenvalues, but the features with the highest eigenvalues may not have the guaranty to provide optimal sensitivity for the classifier. To avoid this problem, an optimization method is required. Evolutionary optimization approach like genetic algorithm (GA) has been used to search the most discriminative subset of transformed features. The particle swarm optimization (PSO) is another optimization approach based on the behavioral study of animals/birds. Therefore, in this paper a feature subset selection based on PSO is proposed which provides better performance as compared to GA.

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A Rule Based Approach for Attribute Selection and Intrusion Detection in Wireless Sensor Networks

Cited by 22 publications

References 3 publications

A multi-measure feature selection algorithm for efficacious intrusion detection

A multi-measure feature selection algorithm for efficacious intrusion detection

Intelligent Intrusion Detection Techniques for Secure Communications in Wireless Networks: A Survey

Feature Selection Using Particle Swarm Optimization in Intrusion Detection

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