Categorical Robustness Assessment for Machine Learning Based Network Intrusion Detection Systems

Network Intrusion Detection Systems NIDS heavily utlize Machine Learning ML but ML models can be manipulated via adversarial attacks. These attacks add carefully crafted perturbations to network traffic data that leads to misclassifications. While prior work has demonstrated adversarial...

Incremental Federated Learning for Intrusion Detection in IoT Networks under Evolving Threat Landscape

The expansion of Internet of Things IoT devices has increased the attack surface of networks, necessitating a robust and adaptive intrusion detection systems. Machine learning based systems have been considered promising in enhancing the detection performance. Federated learning settings enabled ...