Convolutional and Transformer-Based Deep Learning Architectures for Real-Time Anomaly Detection in Network Traffic
Nalini Poornima Suresh, V. Vallinayagi, S. Nanthini · 2025
The rapid expansion of digital infrastructures has led to an unprecedented increase in cyber threats, necessitating advanced techniques for real-time anomaly detection in network traffic. Traditional rule-based and statistical methods often fail to detect sophisticated attacks due to their reliance on predefined signatures and limited adaptability to evolving threats. Deep learning has emerged as a promising alternative, leveraging data-driven approaches to enhance detection accuracy. Convolutional Neural Networks (CNNs) have demonstrated efficiency in extracting spatial-temporal patterns from network traffic, while Transformer-based architectures excel in capturing long-range dependencies and sequential anomalies. However, existing solutions face challenges related to scalability, computational overhead, imbalanced datasets, and adversarial robustness. This chapter provides a comprehensive analysis of CNN and Transformer-based deep learning architectures for real-time anomaly detection, highlighting their strengths, limitations, and practical deployment challenges. A hybrid approach integrating CNNs and Transformers is explored to enhance detection performance by combining local feature extraction with global sequence modeling. the role of synthetic data augmentation, adaptive learning techniques, and adversarial defense mechanisms in improving model generalization and resilience is examined. Future research directions focus on explainable AI, lightweight models for real-time applications, and self-supervised learning for mitigating data scarcity. The insights presented in this chapter contribute to the advancement of AI-driven cybersecurity solutions, enabling proactive threat detection and risk mitigation in dynamic network environments.