Optimizing Intrusion Detection System Performance Through Synergistic Hyperparameter Tuning and Advanced Data Processing

TL;DR

This paper presents a comprehensive approach combining deep learning, data balancing, feature reduction, and hyperparameter tuning to significantly improve intrusion detection system accuracy on large network datasets.

Contribution

It introduces an integrated system that synergistically combines multiple advanced techniques for enhanced intrusion detection performance.

Findings

VGG19 ensemble achieves over 99% accuracy on CIC datasets.

The combined methods outperform traditional intrusion detection approaches.

Models demonstrate strong generalization across datasets.

Abstract

Intrusion detection is vital for securing computer networks against malicious activities. Traditional methods struggle to detect complex patterns and anomalies in network traffic effectively. To address this issue, we propose a system combining deep learning, data balancing (K-means + SMOTE), high-dimensional reduction (PCA and FCBF), and hyperparameter optimization (Extra Trees and BO-TPE) to enhance intrusion detection performance. By training on extensive datasets like CIC IDS 2018 and CIC IDS 2017, our models demonstrate robust performance and generalization. Notably, the ensemble model "VGG19" consistently achieves remarkable accuracy (99.26% on CIC-IDS2017 and 99.22% on CSE-CIC-IDS2018), outperforming other models.