Deep Learning-Based DDoS-Attack Detection for Cyber-Physical System Over 5G Network

With the advent of 5G, cyber-physical systems (CPSs) employed in the vertical industries and critical infrastructures will depend on the cellular network more than ever; making their attack surface wider. Hence, guarding the network against cyberattacks is critical not only for its primary subscribers but to prevent it from being exploited as a proxy to attack CPSs. In this article, we propose a consolidated framework, by utilizing deep convolutional neural networks (CNNs) and real network data, to provide early detection for distributed denial-of-service (DDoS) attacks orchestrated by a botnet that controls malicious devices. These puppet devices individually perform silent call, signaling, SMS spamming, or a blend of these attacks targeting call, Internet, SMS, or a blend of these services, respectively, to cause a collective DDoS attack in a cell that can disrupt CPSs' operations. Our results demonstrate that our framework can achieve higher than 91% normal and underattack cell detection accuracy.

Automated summary

The article proposes a comprehensive framework utilizing deep convolutional neural networks and real network data for early detection of DDoS attacks orchestrated by botnets, achieving over 91% accuracy in detecting normal and under attack cells.