ARCHIVES

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.