Optimized deep learning with binary PSO for intrusion detection on CSE-CIC-IDS2018 dataset
Joint Authors
al-Alusi, Nida Fulayyih Hasan
Mawlud, Abir Tariq
Farhan, Rawah Ismail
Source
al-Qadisiyah Journal for Computer Science and Mathematics
Issue
Vol. 12, Issue 3 (30 Sep. 2020), pp.16-27, 12 p.
Publisher
University of al-Qadisiyah College of computer Science and Information Technology
Publication Date
2020-09-30
Country of Publication
Iraq
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Abstract EN
Anomaly detection is a term refer to any abnormal behaviors, comprise security breaches of network.
Deep Learning (DL)has proven its outperformance compared to machine learning algorithms in solving the complex problems of real-world like intrusion detection.
Though, this approach need more computational resources and consumes long time.
Feature selection is play significant role of choosing the best features that describes the target concept optimally during a classification process.
However, when handle large number of features the selecting of such relevant features becomes a difficult task.
Thus, this paper proposes using Binary Particle Swarm Optimization (BPSO) to solve the feature selection problem.
Then, features selected from BPSO are evaluated on Deep Neural Networks (DNN) classifiers and the CSE-CIC-IDS2018 dataset.
The result of the proposed model has shown comparable performance based on processing time, detection rate and false alarm rate comparing with other benchmark classifiers.
Experimental results have shown a high accuracy of 95%.
American Psychological Association (APA)
Farhan, Rawah Ismail& Mawlud, Abir Tariq& al-Alusi, Nida Fulayyih Hasan. 2020. Optimized deep learning with binary PSO for intrusion detection on CSE-CIC-IDS2018 dataset. al-Qadisiyah Journal for Computer Science and Mathematics،Vol. 12, no. 3, pp.16-27.
https://search.emarefa.net/detail/BIM-1266389
Modern Language Association (MLA)
Farhan, Rawah Ismail…[et al.]. Optimized deep learning with binary PSO for intrusion detection on CSE-CIC-IDS2018 dataset. al-Qadisiyah Journal for Computer Science and Mathematics Vol. 12, no. 3 (2020), pp.16-27.
https://search.emarefa.net/detail/BIM-1266389
American Medical Association (AMA)
Farhan, Rawah Ismail& Mawlud, Abir Tariq& al-Alusi, Nida Fulayyih Hasan. Optimized deep learning with binary PSO for intrusion detection on CSE-CIC-IDS2018 dataset. al-Qadisiyah Journal for Computer Science and Mathematics. 2020. Vol. 12, no. 3, pp.16-27.
https://search.emarefa.net/detail/BIM-1266389
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 26-27
Record ID
BIM-1266389