Network Attack

Network attacks pose a significant threat to digital systems, and research focuses on developing robust intrusion detection systems (IDS) to mitigate this risk. Current efforts leverage machine learning, particularly deep neural networks and ensemble methods, often applied to NetFlow data or raw network packets, to improve detection accuracy and speed, including early detection capabilities before significant damage occurs. These advancements aim to enhance the effectiveness of IDS, addressing challenges like class imbalance, the detection of novel attacks, and the need for efficient processing of large datasets, ultimately improving cybersecurity defenses.