Cryptology ePrint Archive: Report 2020/020

Practical Encrypted Network Traffic Pattern Matching for Secure Middleboxes

Shangqi Lai and Xingliang Yuan and Shi-Feng Sun and Joseph K. Liu and Ron Steinfeld and Amin Sakzad and Dongxi Liu

Abstract: Network Function Virtualisation (NFV) advances the adoption of composable software middleboxes. Accordingly, cloud data centres become major NFV vendors for enterprise traffic processing. Due to the privacy concern of traffic redirection to the cloud, secure middlebox systems (e.g., BlindBox) draw much attention; they can process encrypted packets against encrypted rules directly. However, most of the existing systems supporting pattern matching based network functions require the enterprise gateway to tokenise packet payloads via sliding windows. Such tokenisation induces a considerable communication overhead, which can be over 100$\times$ to the packet size. To overcome this bottleneck, in this paper, we propose the first bandwidth-efficient encrypted pattern matching protocol for secure middleboxes. We resort to a primitive called symmetric hidden vector encryption (SHVE), and propose a variant of it, aka SHVE+, to achieve constant and moderate communication cost. To speed up, we devise encrypted filters to reduce the number of accesses to SHVE+ during matching highly. We formalise the security of our proposed protocol and conduct comprehensive evaluations over real-world rulesets and traffic dumps. The results show that our design can inspect a packet over 20k rules within 100 $\mu$s. Compared to prior work, it brings a saving of 94$\%$ in bandwidth consumption.

Category / Keywords: applications / Privacy-Preserving; Middlebox; Pattern Matching

Original Publication (in the same form): IEEE Transactions on Dependable and Secure Computing
DOI:
10.1109/TDSC.2021.3065652

Date: received 6 Jan 2020, last revised 17 Apr 2021

Contact author: shangqi lai at monash edu

Available format(s): PDF | BibTeX Citation

Version: 20210417:090403 (All versions of this report)

Short URL: ia.cr/2020/020


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