Based NIDS

Machine learning-based Network Intrusion Detection Systems (NIDS) aim to automatically identify malicious network activity, improving upon traditional methods. Current research focuses on enhancing the performance and robustness of these systems, exploring various model architectures like decision trees, random forests, support vector machines, and deep learning models, as well as addressing challenges such as adversarial attacks and data efficiency. A key trend involves improving model reliability through techniques like ensemble methods, data integration, and uncertainty quantification, ultimately striving for more accurate and dependable intrusion detection. This work has significant implications for cybersecurity, potentially leading to more effective and resilient network defenses.