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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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
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author Iqbal H. Sarker
Yoosef B. Abushark
Fawaz Alsolami
Asif Irshad Khan
author_facet Iqbal H. Sarker
Yoosef B. Abushark
Fawaz Alsolami
Asif Irshad Khan
author_sort Iqbal H. Sarker
collection DOAJ
description 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.
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spelling doaj.art-0b0c65d7e53741afa0c0269212c4a3512023-11-19T23:34:37ZengMDPI AGSymmetry2073-89942020-05-0112575410.3390/sym12050754IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection ModelIqbal H. Sarker0Yoosef B. Abushark1Fawaz Alsolami2Asif Irshad Khan3Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, VIC 3122, AustraliaComputer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaComputer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaComputer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaCyber 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.https://www.mdpi.com/2073-8994/12/5/754cybersecuritycyber-attacksanomaly detectionintrusion detection systemmachine learningnetwork behavior analysis
spellingShingle Iqbal H. Sarker
Yoosef B. Abushark
Fawaz Alsolami
Asif Irshad Khan
IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model
Symmetry
cybersecurity
cyber-attacks
anomaly detection
intrusion detection system
machine learning
network behavior analysis
title IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model
title_full IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model
title_fullStr IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model
title_full_unstemmed IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model
title_short IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model
title_sort intrudtree a machine learning based cyber security intrusion detection model
topic cybersecurity
cyber-attacks
anomaly detection
intrusion detection system
machine learning
network behavior analysis
url https://www.mdpi.com/2073-8994/12/5/754
work_keys_str_mv AT iqbalhsarker intrudtreeamachinelearningbasedcybersecurityintrusiondetectionmodel
AT yoosefbabushark intrudtreeamachinelearningbasedcybersecurityintrusiondetectionmodel
AT fawazalsolami intrudtreeamachinelearningbasedcybersecurityintrusiondetectionmodel
AT asifirshadkhan intrudtreeamachinelearningbasedcybersecurityintrusiondetectionmodel