Rademics Research Institute

Abstract

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.  

Introduction

The exponential growth of networked systems and digital infrastructures has significantly increased the attack surface for cyber threats, making real-time anomaly detection a crucial aspect of modern cybersecurity. Traditional intrusion detection systems (IDS) rely on rule-based or statistical anomaly detection methods, which often struggle to detect novel and sophisticated attacks. These conventional approaches are inherently limited by their dependence on predefined signatures and manually crafted feature sets, making them ineffective against zero-day attacks, polymorphic malware, and adversarial exploits. As cyber threats continue to evolve in complexity, security frameworks must transition toward data-driven methodologies that can autonomously learn patterns from vast volumes of network traffic. Deep learning, with its ability to extract meaningful representations from high-dimensional data, has emerged as a promising solution to address these limitations in network anomaly detection. 

Convolutional Neural Networks (CNNs) have demonstrated strong capabilities in analyzing structured network traffic by capturing spatial and temporal correlations within network flow data. By leveraging hierarchical feature extraction, CNNs can detect localized patterns associated with malicious behaviors, making them highly effective for anomaly detection in network security. However, CNNs have inherent limitations when dealing with long-range dependencies, as they primarily focus on local feature extraction. This drawback restricts their ability to detect complex attack patterns that unfold over extended time sequences, such as multi-stage intrusions or advanced persistent threats (APTs). To overcome this challenge, researchers have explored alternative deep learning architectures that can capture both short-term and long-term dependencies within network traffic.