A Data Classification Model: For Effective Classification of Intrusion in an Intrusion Detection System Based on Decision Tree Learning Algorithm

Mehrotra, Latika; Saxena, Prashant Sahai; Doohan, Nitika Vats

doi:10.1007/978-981-10-3932-4_7

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…Mehrotra, Latika, Prashant Sahai Saxena, and Nitika Vats Doohan [1] a novel model is presented here with decision tree concepts for the data classification. Model that is suggested in this paper is based on the updated ID3 method.…”

Section: Literature Survey On Association Rule Miningmentioning

confidence: 99%

Association Rule Mining for Intrusion Detection System: A Survey

Selvamani¹,

Selvi²

2019

AJEAT

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Many modern intrusion detection systems are based on data mining and database-centric architecture, where a number of data mining techniques have been found. Among the most popular techniques, association rule mining is one of the important topics in data mining research. This approach determines interesting relationships between large sets of data items. This technique was initially applied to the so-called market basket analysis, which aims at finding regularities in shopping behaviour of customers of supermarkets. In contrast to dataset for market basket analysis, which takes usually hundreds of attributes, network audit databases face tens of attributes. So the typical Apriori algorithm of association rule mining, which needs so many database scans, can be improved, dealing with such characteristics of transaction database. In this paper, a literature survey on the Association Rule Mining has carried out.

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Section: Literature Survey On Association Rule Miningmentioning

confidence: 99%

Association Rule Mining for Intrusion Detection System: A Survey

Selvamani¹,

Selvi²

2019

AJEAT

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“…Here, the reduced dataset from the above stage is given as input for the classification. Fuzzy Logic [14] has employed to abate the cataloging disputes in the classification phase.…”

Section: Stagementioning

confidence: 99%

“…In order to custom a feed-forward multi-layer in MLP, the pool of non-linear neurons is associated to one another. As a result, this method is branded to be expedient for forecast and classification issues [14]. On the whole, the training of the MLP initiated from a small number of neurons, and with only one hidden layer, which processes the error ratio of the trained BP on holdout samples, gradually aggregate the number of neurons at the hidden layer in which the performance of the trained phase on holdout samples has arisen to go down due to the tricky of overtraining.…”

Section: B Enhanced Artificial Neural Network (Eann) Classificationmentioning

confidence: 99%

A Signature Based Intrusion Detection System with HPFSM and Fuzzy Based Classification Method (IDSFSC)

Latha¹,

Prakash²

2019

AJEAT

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Securing a network from the attackers is a challenging task at present as many users involve in variety of computer networks. To protect any individual host in a network or the entire network, some security system must be implemented. In this case, the Intrusion Detection System (IDS) is essential to protect the network from the intruders. The IDS has to deal with a lot of network packets with different characteristics. A signature-based IDS is a potential tool to understand former attacks and to define suitable method to conquest it in variety of applications. This research article elucidates the objective of IDS with a mechanism which combines the network and host-based IDS. The benchmark dataset for DARPA is considered to generate the IDS mechanism. In this paper, a frame work IDSFSC – signature-based IDS with high pertinent feature selection method is framed. This frame work consists of earlier proposed Feature Selection Method (HPFSM with Enhanced Artificial Neural Network (EANN) for classification of nodes or packets in the network, then the signatures or attack rules are configured by implementing Association Rule mining algorithm and finally the rules are restructured using a pattern matching algorithm-Aho-Corasick to ease the rule checking. The metrics classification accuracy, False Positive Rate (FPR) and Precision are checked and proved the proposed frame work’s efficiency.

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A Data Classification Model: For Effective Classification of Intrusion in an Intrusion Detection System Based on Decision Tree Learning Algorithm

Cited by 2 publications

References 4 publications

Association Rule Mining for Intrusion Detection System: A Survey

Association Rule Mining for Intrusion Detection System: A Survey

A Signature Based Intrusion Detection System with HPFSM and Fuzzy Based Classification Method (IDSFSC)

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