Towards Practical Encrypted Network Traffic Pattern Matching for Secure Middleboxes

TL;DR

This paper introduces a bandwidth-efficient encrypted pattern matching protocol for secure middleboxes, significantly reducing communication overhead and enabling fast packet inspection against large rule sets in encrypted environments.

Contribution

It presents the first bandwidth-efficient encrypted pattern matching protocol using a novel SHVE+ primitive, improving performance and security for encrypted network traffic inspection.

Findings

Inspects a packet over 20,000 rules within 100 microseconds.

Achieves 94% reduction in bandwidth consumption compared to prior work.

Supports real-world rulesets and traffic with high efficiency.

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.