Abstract
In the field of cybersecurity, the detection of network intrusions is a pressing challenge, particularly when dealing with imbalanced datasets. This study presents a novel model based on the MINI-VGGNet architecture, tailored specifically for identifying various types of network attacks using the CICIDS2017 dataset. The objective is to enhance detection accuracy while effectively managing the challenges posed by imbalanced data. The proposed model incorporates convolutional layers to capture deep features from network data, allowing for improved classification of 15 distinct classes of attacks, including DoS and DDoS. Experimental results demonstrate that the model achieves high accuracy in classifying common attack types, although challenges remain in accurately identifying specific classes like Web Attack – XSS and SQL Injection. The architecture's efficiency and lower computational demands make it suitable for real-world applications, particularly in resource-constrained environments. The findings indicate that further refinement of data balancing techniques is necessary to improve classification performance across all attack types. Overall, this research showcases the effectiveness of the MINI-VGGNet-Intrusion model in advancing intrusion detection systems and highlights the ongoing need for innovation in methods for handling imbalanced cybersecurity datasets.