Attention-driven multi-model architecture for unbalanced network traffic intrusion detection via extreme gradient boosting

Attention-driven multi-model architecture for unbalanced network traffic intrusion detection via extreme gradient boosting

Network Intrusion Detection Systems (NIDS) face significant challenges in identifying rare attack instances due to the inherent class imbalance and diversity in network traffic. This imbalance, often characterized by a dominance of benign network traffic data, reduces the effectiveness of traditiona...

Full description

Saved in:
Bibliographic Details
Main Authors: Oluwadamilare Harazeem Abdulganiyu, Taha Ait Tchakoucht, Ahmed El Hilali Alaoui, Yakub Kayode Saheed
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Intelligent Systems with Applications
Subjects:
Intrusion detection system
Artificial intelligence
Imbalanced network traffic
Attention mechanism
Variational autoencoder
Class wise focal loss
Online Access:http://www.sciencedirect.com/science/article/pii/S2667305325000456
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://www.sciencedirect.com/science/article/pii/S2667305325000456

Similar Items