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Research Article
Javad Hamidzadeh
Sadjad University of Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6493-0539
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An intrusion detection system is a security system that aims to detect sabotage and intrusions on networks to inform experts of the attack and abuse of the network. Different classification methods have been used in the intrusion detection systems such as fuzzy, genetic algorithms, decision trees, artificial neural networks, and support vector machines. Moreover, ensemble classifiers have shown more robust and effective performance for various tasks in the field. In this paper, we adopt ensemble models in order to improve the performance of intrusion detection and, at the
same time, decrease the false alarm rate. We use kNN for multi-class classification, as well as SVM to approach the classification problem in normal-based detection. In order to combine multiple outputs, we use the Dempster-Shafer method in which there is the possibility of explicit retrieval of uncertainty. Moreover, we utilize deep learning for extracting features to train the samples, selected by the sample selection algorithm based on ensemble margin. We compare our results with state-of-the-art methods on benchmarking datasets such as UNSW-NB15, CICIDS2017, and NSLKDD. Our proposed method indicate the superiority in terms of prominent metrics Accuracy, Precision, Recall, and F-measure.
Keywords
Intrusion detection system, Dempster Shafer, Deep learning, Ensemble margin, Classification
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CURRENT STATUS: Published
View the published article at Soft Computing.
Version 1
Posted 18 Jun, 2021
No community comments so far
Editorial decision: Accept
On 21 Jul, 2021
Reviewers invited
Invitations sent on 13 Jun, 2021
Reviews received
Received 13 Jun, 2021
Editor assigned
On 03 Jun, 2021
First submitted
On 03 Jun, 2021
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at Soft Computing.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Javad Hamidzadeh
Sadjad University of Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6493-0539
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Soft Computing.
Version 1
Posted 18 Jun, 2021
No community comments so far
Editorial decision: Accept
On 21 Jul, 2021
Reviewers invited
Invitations sent on 13 Jun, 2021
Reviews received
Received 13 Jun, 2021
Editor assigned
On 03 Jun, 2021
First submitted
On 03 Jun, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Soft Computing.
An intrusion detection system is a security system that aims to detect sabotage and intrusions on networks to inform experts of the attack and abuse of the network. Different classification methods have been used in the intrusion detection systems such as fuzzy, genetic algorithms, decision trees, artificial neural networks, and support vector machines. Moreover, ensemble classifiers have shown more robust and effective performance for various tasks in the field. In this paper, we adopt ensemble models in order to improve the performance of intrusion detection and, at the
same time, decrease the false alarm rate. We use kNN for multi-class classification, as well as SVM to approach the classification problem in normal-based detection. In order to combine multiple outputs, we use the Dempster-Shafer method in which there is the possibility of explicit retrieval of uncertainty. Moreover, we utilize deep learning for extracting features to train the samples, selected by the sample selection algorithm based on ensemble margin. We compare our results with state-of-the-art methods on benchmarking datasets such as UNSW-NB15, CICIDS2017, and NSLKDD. Our proposed method indicate the superiority in terms of prominent metrics Accuracy, Precision, Recall, and F-measure.
The full text of this article is available to read as a PDF.
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