Journal article Open Access

# Ensemble of Rule Learner and Sequential Minimum Optimization Algorithm for Intrusion Detection System

D. P. Gaikwad; M. M. Swami; S. S. Kolte

### DataCite XML Export

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<identifier identifierType="URL">https://zenodo.org/record/5602374</identifier>
<creators>
<creator>
<affiliation>Assistant Professor, Computer Science and  Engineering, AISSMS College of Engineering, SPPU Pune, India.</affiliation>
</creator>
<creator>
<creatorName>M. M. Swami</creatorName>
<affiliation>Assistant Professor, Computer Science and  Engineering, AISSMS College of Engineering, SPPU Pune, India.</affiliation>
</creator>
<creator>
<creatorName>S. S. Kolte</creatorName>
<affiliation>Assistant Professor, Computer Science and  Engineering, AISSMS College of Engineering, SPPU Pune, India.</affiliation>
</creator>
</creators>
<titles>
<title>Ensemble of Rule Learner and Sequential  Minimum Optimization Algorithm for Intrusion  Detection System</title>
</titles>
<publisher>Zenodo</publisher>
<publicationYear>2019</publicationYear>
<subjects>
<subject>AdaBoost, Bagging, Combination rule, PART, SMO, True positive and False positive</subject>
<subject subjectScheme="issn">2249-8958</subject>
</subjects>
<contributors>
<contributorName>Blue Eyes Intelligence Engineering  &amp; Sciences Publication(BEIESP)</contributorName>
<affiliation>Publisher</affiliation>
</contributor>
</contributors>
<dates>
<date dateType="Issued">2019-12-30</date>
</dates>
<language>en</language>
<resourceType resourceTypeGeneral="JournalArticle"/>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5602374</alternateIdentifier>
</alternateIdentifiers>
<relatedIdentifiers>
<relatedIdentifier relatedIdentifierType="ISSN" relationType="IsCitedBy" resourceTypeGeneral="JournalArticle">2249-8958</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.35940/ijeat.A9559.129219</relatedIdentifier>
</relatedIdentifiers>
<rightsList>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
</rightsList>
<descriptions>
<description descriptionType="Abstract">&lt;p&gt;An intrusion detection system is a process which automates analyzing activities in network or a computer system. It is used to detect nasty code, hateful activities, intruders and uninvited communications over the Internet. The general intrusion detection system is struggling with some problems like false positive rate, false negative rate, low classification accuracy and slow speed. Now-a-days, this has turned an attention of many researchers to handle these issues. Recently, ensemble of different base classifier is widely used to implement intrusion detection system. In ensemble method of machine learning, the proper selection of base classifier is a challenging task. In this paper, machine learning ensemble have designed and implemented for the intrusion detection system. The ensemble of Partial Decision Tree and Sequential Minimum optimization algorithm to train support vector machine have used for intrusion detection system. Partial Decision Tree rule learner is simplicity and it generates rules fast. Sequential Minimum optimization algorithm is easy to use and is better scaling with training set size with less computational time. Due to these advantages of both classifiers, they jointly used with different methods of ensemble. We make use of all types of methods of ensemble. The performances of base classifiers have evaluated in term of false positive, accuracy and true positive. Performance results display that proposed majority voting method of ensemble using Partial Decision Tree rule learner and Sequential Minimum optimization algorithm based Support Vector Machine offers highest classification among different ensemble classifiers on training dataset. This method of ensemble exhibits highest true positive and lowest false positive rates. It is also observed that stacking of both PART and SMO exhibits lowest and same classification accuracy on test dataset.&lt;/p&gt;</description>
</descriptions>
</resource>

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