Comparative Study of BiGRU with Multi-Head Attention and CNN for Network Intrusion Detection Using a Cleaned and Balanced CSE-CIC-IDS 2018 Dataset

With the age of advanced cyber attacks, robust intrusion detection systems are inevitable in order to protect the network from insecurity. This work presents a new comparative performance evaluation of two deep learning models, namely, Bidirectional Gated Recurrent Unit with Multi Head Attention (BiGRU + MHA) and Convolutional Neural Network (CNN), on the updated CSE-CIC-IDS 2018 dataset (Version 1, 2024). The data set was cleaned and balanced meticulously by eliminating duplicate entries and a two-stage resampling method with random undersampling accompanied with synthetic minority oversampling for accurate representation of both frequent as well as infrequent types of attacks. The experimental results confirm that both models provided superior detection performance, with BiGRU + MHA consistently outperforming CNN. Specifically, BiGRU + MHA provided 99.65 percent accuracy as well as ROC AUC of 99.71 percent, whereas CNN provided 98.85 percent accuracy as well as ROC AUC of 98.92 percent. The observations identify the advantage of using the combination of temporal sequence modeling as well as attention for identifying advanced intrusion patterns in network traffic. Generally, the results confirm that the use of deep temporal learning in combination with structured preparation of the data holds the capability for leading to highly effective intrusion detection, with great potential for strengthening cybersecurity solutions.