Network Intrusion Detection

Network intrusion detection systems (NIDS) aim to identify malicious activities within computer networks, safeguarding against data breaches and system compromise. Current research heavily emphasizes machine learning, particularly deep learning architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs), often combined with techniques like contrastive learning and transfer learning to improve accuracy and adaptability to evolving threats. A key focus is enhancing robustness against adversarial attacks and addressing challenges like imbalanced datasets and the need for explainable AI in NIDS. These advancements are crucial for improving the security and resilience of both individual systems and large-scale networks.