Enhancing Large-Scale Network Security with a VGG-Net-Based DCNN

What is it about?

Abstract— 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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Photo by CHUTTERSNAP on Unsplash

Photo by CHUTTERSNAP on Unsplash

Why is it important?

The increasing complexity of modern networks, driven by the rapid adoption of cloud computing, Internet of Things (IoT), and 5G technologies, has led to an exponential rise in cyber threats as given in [1]. Traditional anomaly detection techniques, such as rule-based systems, statistical thresholding, and signature-based methods, have been widely used to secure network infrastructures [2]. However, these approaches struggle to detect sophisticated and evolving cyber-attacks such as Advanced Persistent Threats (APTs), zero-day exploits, and polymorphic malware as given in [3]. Their reliance on static rules and predefined attack signatures makes them ineffective against novel or previously unseen attack patterns, leading to high false positive rates and limited scalability in large-scale environments. In contrast, Deep Learning (DL)-based anomaly detection models, particularly Deep Convolutional Neural Networks (DCNNs), offer a more effective alternative. Unlike traditional methods, DCNNs can automatically learn complex feature representations from raw network traffic data without relying on manually engineered rules as mentioned in [4]. This enables them to detect both known and previously unseen attack vectors with higher accuracy and adaptability. Among various deep learning architectures, VGG-Net has proven to be highly effective due to its hierarchical feature extraction capabilities, making it particularly well-suited for detecting anomalies in high-dimensional network traffic. This research presents a VGG-based DCNN framework for anomaly detection in large-scale networks, leveraging the TON_IoT 2020 dataset, which includes diverse network and web-based threats. The proposed approach effectively addresses three critical challenges in anomaly detection: (1) data imbalance, (2) real-time scalability, and (3) adaptive threat detection. By integrating advanced feature extraction techniques and real-time data pipelines, the model achieves high detection accuracy (98.27%) with low false positives (2%), making it suitable for enterprise, cloud, and IoT-based cybersecurity applications. This study proposed a VGG-Net-based Deep Convolutional Neural Network (DCNN) framework for large-scale anomaly detection, demonstrating high accuracy (98.27%), low false positive rate (2%), and real-time detection capabilities. The model effectively handles both network-based and web-based cyber threats, making it a scalable and adaptable solution for enterprise, cloud, and IoT-based cybersecurity environments. By leveraging advanced feature extraction techniques and addressing data imbalance, the proposed approach significantly improves threat detection precision while ensuring low-latency processing, making it suitable for deployment in dynamic network infrastructures. While the model achieves state-of-the-art performance, practical implementation in resource-constrained environments such as IoT and edge networks remains a challenge due to computational overhead. Future work will focus on model compression techniques such as quantization, pruning, and knowledge distillation to optimize performance without compromising accuracy. Additionally, integrating Explainable AI (XAI) will enhance model transparency, providing interpretable threat detection insights for cybersecurity professionals. Another key research direction will involve strengthening adversarial robustness through adversarial training and robust feature extraction mechanisms, ensuring resilience against evolving cyber threats and adversarial attacks. By addressing these challenges, the proposed model can be further refined for widespread adoption in real-world security applications.

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