Deep Learning Approaches for Intrusion Detection in Intelligent Communication Networks; A Study

Abstract

The increasing integration of intelligent communication networks, including fifth-generation (5G), sixth-generation (6G), Internet of Things (IoT), cloud computing, edge intelligence, and software-defined networking, has significantly improved communication efficiency and connectivity. However, the rapid expansion of these advanced network infrastructures has also increased vulnerability to sophisticated cyberattacks, malicious intrusions, and security breaches. Traditional intrusion detection systems (IDS), which primarily rely on signature-based and rule-based mechanisms, often fail to identify emerging and complex threats in dynamic communication environments. Deep Learning (DL) approaches have emerged as effective solutions for intrusion detection due to their ability to automatically extract hidden patterns, analyze high-dimensional network traffic data, and detect anomalous behavior in real time. This study explores various deep learning techniques, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Autoencoders, Deep Belief Networks (DBN), and hybrid deep learning models for intrusion detection in intelligent communication networks. The paper examines the role of deep learning in enhancing attack detection accuracy, minimizing false alarm rates, improving scalability, and enabling adaptive threat intelligence in heterogeneous networking environments. Additionally, the research discusses challenges such as data imbalance, adversarial attacks, computational overhead, privacy concerns, and model interpretability in intelligent network security systems. By analyzing recent advancements and research developments, this study highlights the transformative potential of deep learning-based intrusion detection systems in strengthening cybersecurity frameworks for future intelligent communication networks. The findings demonstrate that deep learning techniques significantly improve intrusion detection performance, network resilience, and automated security response mechanisms, contributing to safer and more reliable communication infrastructures.