IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model

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 b...

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Bibliographic Details
Main Authors: Iqbal H. Sarker, Yoosef B. Abushark, Fawaz Alsolami, Asif Irshad Khan
Format: Article
Language:English
Published: MDPI AG 2020-05-01
Series:Symmetry
Subjects:
Online Access:https://www.mdpi.com/2073-8994/12/5/754
Description
Summary: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 <i>intrusion detection</i> system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly <i>machine learning</i> 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 <i>Intrusion Detection Tree (“IntruDTree”)</i> machine-learning-based security model that first takes into account the <i>ranking of security features</i> 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 <i>computational complexity</i> 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.
ISSN:2073-8994