Samia Saidane¹, Francesco Telch², Kussai Shahin² and Fabrizio Granelli¹, ¹University of Trento, Italy, ²Trentino Digitale Spa, Italy

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.

Imbalance Data Processing, Hyper parameter Optimization, Network Intrusion Detection Systems, Deep Learning, Network Traffic Data, NetFlow Data