Network Intrusion

Network intrusion detection aims to identify malicious activities within computer networks, a critical task given the increasing sophistication of cyberattacks. Current research heavily focuses on applying and improving machine learning models, particularly deep learning architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs, such as LSTMs), and transformers, often combined with techniques like graph theory and domain adaptation to enhance robustness and generalization across diverse network environments. These advancements are crucial for improving the accuracy and reliability of intrusion detection systems, ultimately bolstering cybersecurity defenses and mitigating the impact of cyber threats.