CLort: High Throughput and Low Energy Network Intrusion Detection on IoT Devices with Embedded GPUs

Abstract

While IoT is becoming widespread, cyber security of its devices is still a limiting factor where recent attacks (e.g., the Mirai bot-net) underline the need for countermeasures. One commonly-used security mechanism is a Network Intrusion Detection System (NIDS), but the processing need of NIDS has been a significant bottleneck for large dedicated machines, and a show-stopper for resource-constrained IoT devices. However, the topologies of IoT are evolving, adding intermediate nodes between the weak devices on the edges and the powerful cloud in the center. Also, the hardware of the devices is maturing, with new CPU instruction sets, caches as well as co-processors. As an example, modern single board computers, such as the Odroid XU4, come with integrated Graphics Processing Units (GPUs) that support general purpose computing. Even though using all available hardware efficiently is still an open issue, it has the promise to run NIDS more efficiently. In this work we introduce CLort, an extension to the well-known NIDS Snort that (a) is designed for IoT devices (b) alleviates the burden of pattern matching for intrusion detection by offloading it to the GPU. We thoroughly explain how our design is used as part of the latest release of Snort and suggest various optimizations to enable processing on the GPU. We evaluate CLort in regards to throughput, packet drops in Snort, and power consumption using publicly available traffic traces. CLort achieves up to 52% faster processing throughput than its CPU counterpart. CLort can also analyze up to 12% more packets than its CPU counterpart when sniffing a network. Finally, the experimental evaluation shows that CLort consumes up to 32% less energy than the CPU counterpart, an important consideration for IoT devices.