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- Vol. 4 Issue 4
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A Deep Learning Approach for Intrusion Detection Systems –A Review
Authors
Technical Architect, I & I Software Inc, 2571 Baglyos Circle, Suite B-32, Bethlehem, Pennsylvania, USA.
Article Information
- Published In
- Volume 4, Issue 4 (2024)
- Published Online
- July-August 2024
- Pages
- 19-22
- Article Type
- Research Article
References
1. Wu, P. Deep learning for network intrusion detection: Attack recognition with computational intelligence. Master’s Thesis, University of
New South Wales, Sydney, NSW, Australia, 2020
2. Mighan, S.N.; Kahani, M. A novel scalable intrusion detection system based on deep learning. Int. J. Inf. Secur. 2021, 20, 387–403.
3. Liu, H.; Lang, B. Machine learning and deep learning methods for intrusion detection systems: A survey. Appl. Sci. 2019, 9, 4396.
4. Farhan, R.I.; Maolood, A.T.; Hassan, N.F. Optimized deep learning with binary PSO for intrusion detection on CSE-CIC-IDS2018
dataset. J. Al-Qadisiyah Comput. Sci. Math. 2020, 12, 16–27.
5. Bamasag, O.; Alsaeedi, A.; Munshi, A.; Alghazzawi, D.; Alshehri, S.; Jamjoom, A. Real-time DDoS flood attack monitoring and detection
(RT-AMD) model for cloud computing. PeerJ Comput. Sci. 2022, 7, e814.
6. Bhardwaj, A.; Mangat, V.; Vig, R. Hyperband tuned deep neural network with well posed stacked sparse autoencoder for detection of
DDoS attacks in cloud. IEEE Access 2020, 8, 181916–181929
7. Khraisat, A.; Gondal, I.; Vamplew, P.; Kamruzzaman, J.; Alazab, A. Hybrid intrusion detection system based on the stacking ensemble
of c5 decision tree classifier and one class support vector machine. Electronics 2020, 9, 173.
New South Wales, Sydney, NSW, Australia, 2020
2. Mighan, S.N.; Kahani, M. A novel scalable intrusion detection system based on deep learning. Int. J. Inf. Secur. 2021, 20, 387–403.
3. Liu, H.; Lang, B. Machine learning and deep learning methods for intrusion detection systems: A survey. Appl. Sci. 2019, 9, 4396.
4. Farhan, R.I.; Maolood, A.T.; Hassan, N.F. Optimized deep learning with binary PSO for intrusion detection on CSE-CIC-IDS2018
dataset. J. Al-Qadisiyah Comput. Sci. Math. 2020, 12, 16–27.
5. Bamasag, O.; Alsaeedi, A.; Munshi, A.; Alghazzawi, D.; Alshehri, S.; Jamjoom, A. Real-time DDoS flood attack monitoring and detection
(RT-AMD) model for cloud computing. PeerJ Comput. Sci. 2022, 7, e814.
6. Bhardwaj, A.; Mangat, V.; Vig, R. Hyperband tuned deep neural network with well posed stacked sparse autoencoder for detection of
DDoS attacks in cloud. IEEE Access 2020, 8, 181916–181929
7. Khraisat, A.; Gondal, I.; Vamplew, P.; Kamruzzaman, J.; Alazab, A. Hybrid intrusion detection system based on the stacking ensemble
of c5 decision tree classifier and one class support vector machine. Electronics 2020, 9, 173.
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