Optimizing Deep Learning Algorithms for Enhanced Detection Accuracy in Distributed Network Attack Scenarios

Abstract

The proliferation of distributed network attacks poses a significant threat to the security of critical infrastructure. This research investigates optimization strategies for deep learning algorithms to enhance detection accuracy while minimizing false positive rates in large-scale network environments. The study addresses fundamental challenges in coordinated attack detection through systematic feature engineering, architectural optimization, and improvements in training efficiency. Experimental evaluations on CICIDS2017 and UNSW-NB15 datasets demonstrate substantial performance gains, achieving 97.8% detection accuracy with reduced computational overhead. The proposed optimization methodology strikes a balance between detection precision and operational efficiency, offering practical solutions for cloud data centers and enterprise networks. Performance analysis reveals a 23% reduction in false positive rates and a 34% improvement in training convergence speed compared to baseline approaches.