Enhancing Large-Scale Network Security with a VGG-Net-Based DCNN: A Deep Learning Approach to Anomaly Detection
DOI:
https://doi.org/10.18196/jrc.v6i3.25169Keywords:
Anomaly Detection, VGG-Net, Real-Time Detection, Network-Based Attacks, Deep LearningAbstract
Ensuring robust network security in large-scale environments requires real-time, highly accurate anomaly detection. This study introduces a Deep Convolutional Neural Network (DCNN) based on VGG-Net for detecting network-based and web-based cyber threats, including DoS, DDoS, ransomware, SQL injection, and port scans. The model leverages advanced feature extraction and effectively addresses data imbalance through SMOTE-based augmentation and synthetic data generation. Trained on the TON_IoT 2020 dataset, the framework achieves 98.47% training accuracy, 97.94% validation accuracy, and 98.27% testing accuracy, with a false positive rate of 2%, ensuring precise differentiation between normal and malicious traffic. While the model demonstrates high accuracy and real-time scalability, the computational complexity of VGG-Net poses challenges for deployment in resource-constrained IoT and edge computing environments. To mitigate this, future research will explore model compression techniques such as quantization and pruning. Additionally, despite its robustness in detecting complex attack patterns, the model remains susceptible to adversarial attacks, which could compromise detection reliability. To enhance security, adversarial training and Explainable AI (XAI) techniques will be integrated to improve model transparency and resistance to adversarial manipulations. Compared to existing deep learning approaches such as LSTMs, GANs, and autoencoders, the proposed model achieves higher detection accuracy and lower false positive rates, making it a scalable and adaptable solution for enterprise, cloud, and IoT-based cybersecurity applications.
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