A dependable hybrid machine learning model for network intrusion detection

• Published in: Journal of information security and applications Vol. 72; p. 103405
• Main Authors: Talukder, Md. Alamin, Hasan, Khondokar Fida, Islam, Md. Manowarul, Uddin, Md. Ashraf, Akhter, Arnisha, Yousuf, Mohammand Abu, Alharbi, Fares, Moni, Mohammad Ali
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2023
• Subjects: Accuracy; Dependability; Feature importance; Feature selection; Intrusion detection system; Machine learning; XGBoost; Feature importance; Feature selection; Accuracy; Dependability; Intrusion detection system; XGBoost; Machine learning

Network intrusion detection systems (NIDSs) play an important role in computer network security. There are several detection mechanisms where anomaly-based automated detection outperforms others significantly. Amid the sophistication and growing number of attacks, dealing with large amounts of data...