|
Article information
2024 , Volume 29, ¹ 5, p.113-123
Sahib R.H., Jawad D.M., Mtasher A.K., Msad J.J.
Network intrusion detection system using machine learning models and data mining strategies: comprehensive study
Cybersecurity concerns have increased as a result of the development of computating and intelligent gadgets and the growing interconnectivity of numerous systems. This study investigates the use of data mining techniques and machine learning models in building intrusion detection systems for network security. By investigating the use of several machine learning approaches, such as naive Bayes, random forest, support vector machines, decision tree, k-nearest neighbours, and XGBoost, this study seeks to answer this problem. Furthermore, data mining techniques including association rule mining and clustering algorithms are investigated. The network intrusion detection dataset, which can be downloaded from Kaggle, is used to train and evaluate the system. The primary aim of this study is to provide a more effective and adaptable solution to the network intrusion problem, with the ultimate goal of developing a system that can accurately and efficiently detect and respond to network intrusions.
Keywords: intrusion detection system, machine learning models, data mining strategies, network security, naive Bayes, random forest
doi: 10.25743/ICT.2024.29.5.009
Author(s):
Sahib Rihab Habeeb
Position: The master
Office: University of Babylon, Architecture Department
Address: Iraq, Babylon, 60 St, Hillah
E-mail: art.rehab.habeeb@uobabylon.edu.iq
Jawad Duha Husein Mohamed
Position: The master
Office: University of Babylon Information Security Department, College of IT
Address: Iraq, Hay Alsalam, 60 St, Hillah
Phone Office: (964) 7732352388
E-mail: dhuha.mohamedjawad@uobabylon.edu.iq
Mtasher Ashwaq Katham
Position: The master
Office: Al-Furat Al-Awsat Technical University, College of Health and Medical Technologies
Address: 54003, Iraq, Babylon, Najaf
Phone Office: (964) 07802896808
E-mail: ashwaq.hefaz.ckm@atu.edu.iq
Msad J J
Position: The master
Office: Al-Furat Al-Awsat Technical University
Address: Iraq, El-Kufa, Babylon, Najaf
Phone Office: (964) 07706092287
E-mail: jenan.jader@atu.edu.iq
Bibliography link:
Sahib R.H., Jawad D.M., Mtasher A.K., Msad J.J. Network intrusion detection system using machine learning models and data mining strategies: comprehensive study // Computational technologies. 2024. V. 29. ¹ 5. P. 113-123
|
