Fidas

Network intrusion detection systems (IDS) are crucial for secure cloud computing, but they are also severely constrained by CPU computation capacity as the network bandwidth increases. Therefore, hardware offloading is essential for the IDS servers to support the ever-growing throughput demand for packet processing. Based on the experience of large-scale IDS deployment, we find the existing hardware offloading solutions have fundamental limitations that prevent them from being massively deployed in the production environment. In this paper, we present Fidas, an FPGA-based intrusion detection offload system that avoids the limitations of the existing hardware solutions by comprehensively offloading the primary NIC, rule pattern matching, and traffic flow rate classification. The pattern matching module in Fidas uses a multi-level filter-based approach for efficient regex processing, and the flow rate classification module employs a novel dual-stack memory scheme to identify the hot flows under volumetric attacks. Our evaluation shows that Fidas achieves the state-of-the-art throughput in pattern matching and flow rate classification while freeing up processors for other security-related functionalities. Fidas is deployed in the production data center and has been battle-tested for its performance, cost-effectiveness, and DevOps agility.