Boosting Network Security: A comparative Analysis of Deep Learning Techniques for Intrusion Detection

Abstract

This paper addresses demanding situations in Intrusion Detection Systems (IDS) by way of combining the Adaptive Synthetic Sampling (ADASYN) method with a break up-primarily based Resnet framework. ADASYN balances sample distribution, overcoming biases in the direction of large samples. Our method extracts multiscale functions, reduces interchannel redundancy, and enhances the version's capability using a smooth hobby operation. We recommend a Residual Neural Network (ResNN) version for intrusion detection, displaying massive enhancements in recognition accuracy and execution performance. Ongoing work targets to optimize performance further, highlighting the capacity for extra strong IDS solutions.