Cybersecurity Attack Detection Model, Using Machine Learning Techniques

Basit Öğe Kaydı
dc.authoridAVCI, Dr. Isa/0000-0001-7032-8018
dc.contributor.authorAvci, Isa
dc.contributor.authorKoca, Murat
dc.date.accessioned2024-09-29T16:12:25Z
dc.date.available2024-09-29T16:12:25Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractMillions of people use the web every day, in this age of technology and the internet. Protecting the privacy and security of these users is a significant challenge for cybersecurity developers. With tremendous technological advancements, there is a noticeable improvement in the cyber-attackers' capabilities. At the same time, traditional Intrusion Detection Systems (IDS) are no longer effective at detecting intrusions. After the tremendous competences achieved by Artificial Intelligence (AI) techniques in all fields, great interest has developed in its use in the field of cybersecurity. There have been many studies that use Machine Learning (ML)-based intrusion detection systems. Despite the strong performance of ML techniques in detecting malicious activities, some challenges still reduce accuracy of performance. Knowing the proper technique, as well as knowing the features, is essential for effective intrusion detection. Therefore, this study proposes an effective network intrusion detection system based on ML and feature selection techniques. The performance of four ML techniques, the Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and the Decision Tree (DT) systems for intrusion detection are explored. In addition, feature selection techniques are employed for the selection of important features. Among the techniques used, the RF technique achieved the best performance, outperforming other techniques, with an accuracy of 99.72%. This study elaborates on the detection of malicious and benign cyber-attacks, with a new-level, high accuracy.en_US
dc.identifier.endpage44en_US
dc.identifier.issn1785-8860
dc.identifier.issue7en_US
dc.identifier.startpage29en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14619/8751
dc.identifier.volume20en_US
dc.identifier.wosWOS:001000104800002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherBudapest Techen_US
dc.relation.ispartofActa Polytechnica Hungaricaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectcybersecurityen_US
dc.subjectintrusion detectionen_US
dc.subjectDDoS attacksen_US
dc.subjectmachine learningen_US
dc.subjectfeature selection techniquesen_US
dc.titleCybersecurity Attack Detection Model, Using Machine Learning Techniquesen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu