Based Intrusion Detection

Anomaly-based intrusion detection systems aim to identify malicious network activity by learning patterns of normal behavior and flagging deviations. Current research emphasizes improving the accuracy and timeliness of detection, focusing on deep learning architectures like autoencoders and generative adversarial networks (GANs), as well as hybrid approaches combining supervised and unsupervised methods to address both known and unknown threats. Addressing the persistent challenge of high false positive rates and improving the robustness of these systems against adversarial attacks are key areas of ongoing investigation, with significant implications for enhancing cybersecurity across various sectors.