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eBPF is a new technology which allows dynamically loading pieces of code intothe Linux kernel. It can greatly speed up networking since it enables thekernel to process certain packets without the involvement of a userspaceprogram. So far eBPF has been used for simple packet filtering applicationssuch as firewalls or Denial of Service protection. We show that it is possibleto develop a flow based network intrusion detection system based on machinelearning entirely in eBPF. Our solution uses a decision tree and decides foreach packet whether it is malicious or not, considering the entire previouscontext of the network flow. We achieve a performance increase of over 20%compared to the same solution implemented as a userspace program.

A flow-based IDS using Machine Learning in eBPF