标题： RevealNet：可编程网络上用于攻击归因的分布式流量关联 显示英文标题

标题： RevealNet: Distributed Traffic Correlation for Attack Attribution on Programmable Networks

摘要： 网络攻击者越来越多地使用代理链、VPN 和匿名网络来掩盖他们的活动。为了解决这个问题，过去的研究探讨了流量关联技术在攻击归因中的适用性，即识别攻击者的真正网络位置。然而，当前的流量关联方法依赖于配置良好且集中的系统，这些系统从多个网络探针获取流以计算关联分数。不幸的是，这使得关联工作对于大规模高速网络来说扩展性较差。在本文中，我们提出了 RevealNet，这是一种用于攻击归因的去中心化框架，它协调一批 P4 可编程交换机来进行流量关联。RevealNet 基于一组受先前关于计算和比较流素描（流的关键特征的紧凑摘要）工作的启发的关联原语，以实现高效的分布式网络内流量关联。我们的评估表明，RevealNet 在准确性上可与集中式攻击归因系统相媲美，同时显著降低了由关联任务引起的计算复杂度和带宽开销。 显示英文摘要

摘要： Network attackers have increasingly resorted to proxy chains, VPNs, and anonymity networks to conceal their activities. To tackle this issue, past research has explored the applicability of traffic correlation techniques to perform attack attribution, i.e., to identify an attacker's true network location. However, current traffic correlation approaches rely on well-provisioned and centralized systems that ingest flows from multiple network probes to compute correlation scores. Unfortunately, this makes correlation efforts scale poorly for large high-speed networks. In this paper, we propose RevealNet, a decentralized framework for attack attribution that orchestrates a fleet of P4-programmable switches to perform traffic correlation. RevealNet builds on a set of correlation primitives inspired by prior work on computing and comparing flow sketches -- compact summaries of flows' key characteristics -- to enable efficient, distributed, in-network traffic correlation. Our evaluation suggests that RevealNet achieves comparable accuracy to centralized attack attribution systems while significantly reducing both the computational complexity and bandwidth overheads imposed by correlation tasks.