Work-in-Progress: Towards Real-Time IDS via RNN and Programmable Switches Co-Designed Approach

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 real-time deployment, i.e., existing DL solutions are essentially offline analysis. 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.