RIDS: Towards Advanced IDS via RNN Model and Programmable Switches Co-Designed Approaches

Existing Deep Learning (DL)-based network Intrusion Detection System (IDS) is able to characterize sequence semantics of traffic and discover malicious behaviors. Yet DL models are often nonlinear and highly non-convex functions that are difficult for in-network deployment. In this paper, we present RIDS, a hardware-friendly Recurrent Neural Network (RNN) model that is co-designed with programmable switches. As its core, RIDS is powered by two tightly-coupled components: (i) rLearner, the RNN learning module with in-network deployability as the first-class requirement; and (ii) rEnforcer, the concrete pipeline design to realize rLearner-generated models inside the network dataplane. We implement a prototype of RIDS and evaluate it on our physical testbed. The experiments show that RIDS could satisfy both detection performance and high-speed bandwidth adaptation simultaneously, when none of the other existing approaches could do so. Inspiringly, RIDS realizes remarkable intrusion/malware detection effect (e.g., ~99% F1 score) and model deployment (e.g., 100 Gbps per port), while only imposing nanoseconds of latency.