High Performance Network Metadata Extraction Using P4 for ML-based Intrusion Detection Systems

Abstract

Today’s communication networks process an increasing amount of traffic, while simultaneously providing services to a larger and more diverse quantity of devices. This enhances the complexity of the network and imposes a larger attack space, impacting network management and security efforts. Deployed hardware middle-boxes, like firewalls and Intrusion Detection Systems (IDSs) often lack the flexibility to adapt to this dynamic environment, which Network Function Virtualization (NFV) addresses by implementing these services in software. Yet, this may impose a bottleneck, due to the absence of hardware acceleration. To mitigate this drawback, the functionality can be offloaded to programmable hardware, using P4. In this work we implement an IDS, capable of operating in core and backbone networks up to 100Gbps. This is achieved by using the hardware acceleration of P4-enabled Intel© Tofino™ switches for high performance metadata extraction, in order to train an ML-based detection engine. The system is evaluated regarding its throughput and obtainable aggregation levels as well as its accuracy for detecting a variety of network attacks.