Anomaly-based intrusion detection systems using machine learning algorithm

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dc.contributor.advisorUçan, Osman Nuri
dc.contributor.authorAl-Juboori, Karrar Ali Awad
dc.date.accessioned2023-12-21T13:50:04Z
dc.date.available2023-12-21T13:50:04Z
dc.date.issued2023en_US
dc.date.submitted2023
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractThe increasing number of cyber-attacks highlights the need for improved intrusion detection systems (IDS) that can detect and classify attacks accurately and efficiently. Machine learning (ML) techniques have been shown to be effective in this regard, as they can learn from data and identify patterns that may indicate an intrusion or anomaly. In this research, we want to find a highly accurate and error-tolerant classifier for identifying aberrant traffic in a network intrusion detection dataset. We will evaluate the effectiveness of seven different machine learning techniques on a given dataset by using various evaluation metrics such as accuracy, precision, recall, and F1-score. These measurements allow us to evaluate the effectiveness of various algorithms and offer insightful information about the advantages and disadvantages of each approach. Our ultimate goal is to select the best-performing classifier that can accurately detect anomalous traffic with minimal error. By identifying an effective classifier for NIDS, our goal is to aid in the creation of stronger, more dependable systems that can defend against cyberattacks and guarantee the safety of digital networks.en_US
dc.identifier.citationAl-Juboori, K. A. A. (2023). Anomaly-based intrusion detection systems using machine learning algorithm. (Yayınlanmamış yüksek lisans tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4420
dc.identifier.yoktezid826077
dc.institutionauthorAl-Juboori, Karrar Ali Awad
dc.language.isoen
dc.publisherAltınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsüen_US
dc.relation.publicationcategoryTezen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectInstruction Detection System (IDS)en_US
dc.subjectNetwork IDSen_US
dc.subjectMachine Learningen_US
dc.subjectCyber Securityen_US
dc.titleAnomaly-based intrusion detection systems using machine learning algorithm
dc.typeMaster Thesis

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