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IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model

1
Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia
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Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong 4349, Bangladesh
3
Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(5), 754; https://doi.org/10.3390/sym12050754
Received: 31 March 2020 / Revised: 14 April 2020 / Accepted: 15 April 2020 / Published: 6 May 2020
Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model. View Full-Text
Keywords: cybersecurity; cyber-attacks; anomaly detection; intrusion detection system; machine learning; network behavior analysis; cyber decision making; cybersecurity analytics; cyber threat intelligence cybersecurity; cyber-attacks; anomaly detection; intrusion detection system; machine learning; network behavior analysis; cyber decision making; cybersecurity analytics; cyber threat intelligence
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MDPI and ACS Style

Sarker, I.H.; Abushark, Y.B.; Alsolami, F.; Khan, A.I. IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model. Symmetry 2020, 12, 754. https://doi.org/10.3390/sym12050754

AMA Style

Sarker IH, Abushark YB, Alsolami F, Khan AI. IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model. Symmetry. 2020; 12(5):754. https://doi.org/10.3390/sym12050754

Chicago/Turabian Style

Sarker, Iqbal H., Yoosef B. Abushark, Fawaz Alsolami, and Asif I. Khan 2020. "IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model" Symmetry 12, no. 5: 754. https://doi.org/10.3390/sym12050754

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