Passive Data-Plane Telemetry to Mitigate Long-Distance BGP Hijacks

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Authors: Satadal Sengupta, Hyojoon Kim, D. Jubas, Maria D. Apostolaki, Jennifer Rexford
Cited by: 0

Abstract

Poor security of Internet routing enables adversaries to divert user data through unintended infrastructures in attacks known as hijacks. Of particular concern - and the focus of this paper - are cases where attackers reroute domestic traffic through foreign countries and still deliver it to the intended destination, exposing traffic to surveillance, bypassing legal privacy protections, and posing national security threats. Efforts to detect and mitigate such attacks have focused primarily on the control plane, while data-plane signals remain largely overlooked. In this paper, we argue that passively-monitored round-trip time (RTT) - and, in particular, changes in its propagation-delay component - offers a promising signal for detection: the increased propagation delay is unavoidable and directly observable from affected networks, enabling opportunities for faster detection and mitigation. We explore the practicality of using RTT variations for hijack detection, addressing two key questions: (1) What coverage can this provide, given its heavy dependence on the geolocations of the sender, receiver, and adversary? and (2) Can an always-on RTT-based detection system be deployed without disrupting normal network operations? Focusing on cross-country interception attacks, we find that coverage is high: 97% under ideal (i.e., data travels at the speed of light) conditions, and 91% and 86% with real traffic from two datasets. To demonstrate practicality, we design HiDe, which reliably detects delay surges from long-distance hijacks at line rate using commodity programmable hardware. We measure HiDe’s accuracy and false-positive rate on real-world data and validate it with ethically conducted hijacks.

Fields of Study & Subjects

Network Security and Intrusion Detection Network Traffic and Congestion Control Internet Traffic Analysis and Secure E-voting

Publication details

Journal / source
arXiv (Cornell University)
Publication date
1 January 2026
Language
EN
Document type
Preprint