Visible to the public Deep Packet Inspection in FPGAs via Approximate Nondeterministic Automata

TitleDeep Packet Inspection in FPGAs via Approximate Nondeterministic Automata
Publication TypeConference Paper
Year of Publication2019
AuthorsCeška, Milan, Havlena, Vojtech, Holík, Lukáš, Korenek, Jan, Lengál, Ondrej, Matoušek, Denis, Matoušek, Jirí, Semric, Jakub, Vojnar, Tomáš
Conference Name2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)
PublisherIEEE
ISBN Number978-1-7281-1131-5
KeywordsAcceleration, approximate nondeterministic automata, approximate nondeterministic FAs, approximate reduction, approximate reduction techniques, Architecture, Automata, Computer architecture, computer network security, deep packet inspection, field programmable gate arrays, finite automata, FPGA architecture, Hardware, high-speed computer networks, Internet, Internet connection, intrusion detection system, multistage architecture, network intrusion detection systems, pubcrawl, RE matching, regular expression matching, resilience, Resiliency, Scalability, suspicious network traffic, telecommunication traffic, Throughput
Abstract

Deep packet inspection via regular expression (RE) matching is a crucial task of network intrusion detection systems (IDSes), which secure Internet connection against attacks and suspicious network traffic. Monitoring high-speed computer networks (100 Gbps and faster) in a single-box solution demands that the RE matching, traditionally based on finite automata (FAs), is accelerated in hardware. In this paper, we describe a novel FPGA architecture for RE matching that is able to process network traffic beyond 100 Gbps. The key idea is to reduce the required FPGA resources by leveraging approximate nondeterministic FAs (NFAs). The NFAs are compiled into a multi-stage architecture starting with the least precise stage with a high throughput and ending with the most precise stage with a low throughput. To obtain the reduced NFAs, we propose new approximate reduction techniques that take into account the profile of the network traffic. Our experiments showed that using our approach, we were able to perform matching of large sets of REs from SNORT, a popular IDS, on unprecedented network speeds.

URLhttps://ieeexplore.ieee.org/document/8735503
DOI10.1109/FCCM.2019.00025
Citation Keyceska_deep_2019