Abstract
Network security is increasing in importance as systems become more interconnected. Much research has been conducted on large appliances for network security, but these do not scale well to lightweight systems such as those used in the Internet of Things (IoT). Meanwhile, the low power processors used in IoT devices do not have the required performance for detailed packet analysis. We present an approach for network intrusion detection using neural networks, implemented on FPGA SoC devices that can achieve the required performance on embedded systems. The design is flexible, allowing model updates in order to adapt to emerging attacks.