• T. Sreekala
• Mr.P. Yejdani Khan

The cyber intrusion prevention model represents a new means of cyber protection with intelligent defense capability. It can not only detect intrusion behavior but also respond to such behavior in a timely manner. This study applies deep learning theory and semi-supervised clustering to cyber intrusion prevention technology. Deep learning based on deep structures represents the current development trend of neural networks. Semi-supervised learning uses a large amount of unlabeled cyber traffic data and a small amount of labeled cyber traffic data to achieve cyber intrusion prevention with a low recognition error rate. Discriminative deep belief network (DDBN)-based cyber defense technology has emerged as a research hotspot in the field of cyber intrusion prevention owing to its low error rate

Cyber security, discriminative deep belief networks, intrusion prevention, local and non-local ,regularization semi-supervised deep learning

"Cyber Intrusion Prevention for Large-Scale Semi-Supervised Deep Learning Based on Local and Non-Local Regularization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.125-131, June-2024, Available :http://www.jetir.org/papers/JETIRGL06023.pdf

"Cyber Intrusion Prevention for Large-Scale Semi-Supervised Deep Learning Based on Local and Non-Local Regularization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp125-131, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06023.pdf