Correlation-Aware Neural Networks for DDoS Attack Detection In IoT Systems

Understand

We present a comprehensive study on applying machine learning to detect distributed Denial of service (DDoS) attacks using large-scale Internet of Things (IoT) systems.

• While prior works and existing DDoS attacks have largely focused on individual nodes transmitting packets at a high volume, we investigate more sophisticated futuristic attacks that use large numbers of IoT devices and camouflage their attack by having each node transmit at a volume typical of benign traffic.
• We introduce new correlation-aware architectures that take into account the correlation of traffic across IoT nodes, and we also compare the effectiveness of centralized and distributed detection models.
• We extensively analyze the proposed architectures by evaluating five different neural network models trained on a dataset derived from a 4060-node real-world IoT system.