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dc.contributor.authorOzalp, Ahmet Nusret
dc.contributor.authorAlbayrak, Zafer
dc.date.accessioned2023-03-14T20:28:56Z
dc.date.available2023-03-14T20:28:56Z
dc.date.issued2022
dc.identifier.issn1785-8860
dc.identifier.urihttps://hdl.handle.net/20.500.14002/1519
dc.identifier.urihttps://www.researchgate.net/publication/363414992_Detecting_Cyber_Attacks_with_High-Frequency_Features_using_Machine_Learning_Algorithms
dc.description.abstractIn computer networks, intrusion detection systems are used to detect cyber-attacks and anomalies. Feature selection is important for intrusion detection systems to scan the network quickly and accurately. On the other hand, analyzes performed using data with many attributes cause significant resource and time loss. In this study, unlike the literature studies, the frequency effects of the features in the data set are analyzed in detecting cyber-attacks on computer networks. Firstly, the frequencies of the features in the NSL-KDD data set were determined. Then, the effect of high-frequency features in detecting cyber-attacks has been examined with the widely used machine learning algorithms of Random Forest, J48, Naive Bayes, and Multi-Layer Perceptron. The performance of each algorithm is evaluated by considering Precision, False Positive Rate, Accuracy, and True Positive Rate statistics. Detection performances of different types of cyberattacks in the NSL-KDD dataset were analyzed with machine learning algorithms. Precision, Receiver Operator Characteristic, F1 score, recall, and accuracy statistics were chosen as success criteria of machine learning algorithms in attack detection. The results showed that features with high frequency are effective in detecting attacks.en_US
dc.language.isoengen_US
dc.publisherBudapest Techen_US
dc.relation.ispartofActa Polytechnica Hungaricaen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAttribute selectionen_US
dc.subjectCyberattacksen_US
dc.subjectMachine Learningen_US
dc.subjectIDSen_US
dc.subjectNSL-KDDen_US
dc.subjectAnomaly detectionen_US
dc.subjectNetwork Intrusion Detectionen_US
dc.subjectDetection Systemen_US
dc.titleDetecting Cyber Attacks with High-Frequency Features using Machine Learning Algorithmsen_US
dc.typearticleen_US
dc.departmentBelirlenceken_US
dc.identifier.volume19en_US
dc.identifier.issue7en_US
dc.identifier.startpage213en_US
dc.identifier.endpage233en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorwosidALBAYRAK, Zafer/ABH-5699-2020
dc.identifier.wosWOS:000897735300012en_US
dc.identifier.scopus2-s2.0-85138748582en_US


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