Attack’s Feature Selection-Based Network Intrusion Detection System Using Fuzzy Control Language

Ramakrishnan, S.; Devaraju, S.

doi:10.1007/s40815-016-0160-6

Cited by 21 publications

(8 citation statements)

References 20 publications

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“…Rule-based classification is applied to detect the intrusions. The evaluated result shows the improvement in efficiency compared with other techniques [12].…”

Section: Background Studymentioning

confidence: 81%

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Evaluation Of Efficiency For Intrusion Detection System Using Gini Index C5 Algorithm

Devaraju¹

2019

IJEAT

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 Abstract: Security is the critical part in the computers and the networks which connect the computers each other's through network for communication or exchange the data. It is a wide complex to secure the data while transmitting the data between the system/networks. The intrusion detection is a mechanism to protect the data. There are various existing mechanisms for intrusion detection namely neural network, data mining technique, fuzzy logic, statistical technique etc. In this paper, Principal Component Analysis is applied to reduce the features and Gini index C5 algorithm is used to investigate and evaluate the efficiency and false positive rate. The benchmark KDD dataset is used to evaluate the efficiency and minimize the false positive rate using Gini index C5 algorithm and compare with other algorithm which shows significant improvement and to experiment the KDD Dataset to improve the efficiency and minimize the false positive rate using MATLAB software and demonstrated with the KDD dataset.

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“…Rule-based classification is applied to detect the intrusions. The evaluated result shows the improvement in efficiency compared with other techniques [12].…”

Section: Background Studymentioning

confidence: 81%

“…It is an influential tool for analysis and difficult to find the high dimensions. While reducing the number of dimensions and found the patterns then which can be compressed without any loss of dimensions [4,12]. Feature selection is applied to raise the efficiency and decline the false positive rate.…”

Section: Kdd Cup Dataset Descriptionmentioning

confidence: 99%

Evaluation Of Efficiency For Intrusion Detection System Using Gini Index C5 Algorithm

Devaraju¹

2019

IJEAT

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“…In [20] an entropy based feature selection method has been used with layered classifier based on fuzzy rules generated by a layered fuzzy control language. It was found that the layered classifier improved performance and reduced classification time.…”

Section: Related Workmentioning

confidence: 99%

An Intrusion Detection System for Network Security Situational Awareness Using Conditional Random Fields

Mahendiran¹,

Appusamy²

2018

IJIES

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The huge proliferation of cyber economy, social media usage and online transactions has resulted in large volumes of data in the cyber space. This has led to an increase in concern over the security of confidential data in the cyber space. Network security situational awareness systems helps in effectively monitoring a network for suspicious activities and thwarting any attacks on the information stored in the network. In this paper, an intrusion detection system for network security situational awareness using conditional random field has been proposed. Conditional random fields being conditional models are capable of modeling inter relationships between the observed features. This results in greater accuracy in classification. Conditional random field's complexity increases with the number of features in the observation. To reduce this complexity, a feature selection method using oneR algorithm has been proposed. The ability of oneR algorithm to find the best attribute that result in optimal classification has been used for ranking the features in the observation. The proposed system was trained and tested using the bench mark NSL-KDD dataset. The proposed system on experimentation, exhibited higher accuracy (98%) in identifying an attack in general and also showed better performance (>93%) in identifying individual attack categories specifically.

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“…Azad et al [11] introduced an intrusion detection system which is based on the fuzzy min, fuzzy max neural network and the particle swarm optimization. Ramakrishnan et al [12] proposed an entropy-based feature selection to select the important features, layered fuzzy control language to generate fuzzy rules, and layered classifier to detect various network attacks namely neptune, smurf, back, and mailbomb. However, no research could be found on fuzzy methods of detecting exploitation within a single system.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy zero day exploits detector system

Shaout

Smyth²

2017

IJACR

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Attack’s Feature Selection-Based Network Intrusion Detection System Using Fuzzy Control Language

Cited by 21 publications

References 20 publications

Evaluation Of Efficiency For Intrusion Detection System Using Gini Index C5 Algorithm

Evaluation Of Efficiency For Intrusion Detection System Using Gini Index C5 Algorithm

An Intrusion Detection System for Network Security Situational Awareness Using Conditional Random Fields

Fuzzy zero day exploits detector system

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