Volumetric Attack

Volumetric attacks, characterized by overwhelming network resources with high traffic volumes, pose a significant threat to Internet of Things (IoT) networks. Current research focuses on developing robust anomaly detection systems, often employing machine learning models like one-class classifiers and decision tree ensembles, to identify these attacks amidst legitimate network activity. A key challenge lies in improving the resilience of these models against sophisticated, adversarial volumetric attacks designed to evade detection. This research is crucial for enhancing the security of increasingly interconnected IoT ecosystems and informing the development of more effective cybersecurity strategies.