Network Intrusion Detection Using Machine Learning Techniques

• Autorzy: Almutairi Yasmeen S. , Alhazmi Bader , Munshi Amr

Identyfikatory

DOI

10.12913/22998624/149934

• Języki publikacji: EN

Abstrakty

EN

Intrusion detection systems (IDS) are essential for the protection of advanced communication networks. These systems were primarily designed to identify particular patterns, signatures, and rule violations. Machine Learning and Deep Learning approaches have been used in recent years in the field of network intrusion detection to provide promising alternatives. These approaches can discriminate between normal and anomalous patterns. In this paper, the NSL-KDD (Network Security Laboratory Knowledge Discovery and Data Mining) benchmark data set has been used to evaluate Network Intrusion Detection Systems (NIDS) by using different machine learning algorithms such as Support Vector Machine, J48, Random Forest, and Naïve Bytes with both binary and multi-class classification. The results of the application of those techniques are discussed in details and outperformed previous works.

Słowa kluczowe

EN

data mining; intrusion detection system; machine learning techniques

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2022
• Tom: Vol. 16, no 3
• Strony: 193--206
• Opis fizyczny: Bibliogr. 29 poz., fig., tab.

Twórcy

autor

Almutairi Yasmeen S.

• Computer Engineering Department, Umm Al-Qura University, Makkah 21961, Saudi Arabia

autor

Alhazmi Bader

• Computer Engineering Department, Umm Al-Qura University, Makkah 21961, Saudi Arabia

autor

Munshi Amr

• Computer Engineering Department, Umm Al-Qura University, Makkah 21961, Saudi Arabia

• https://orcid.org/0000-0002-4002-3755

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).