Christoph Sendner (University of Würzburg), Jasper Stang (University of Würzburg), Alexandra Dmitrienko (University of Würzburg), Raveen Wijewickrama (University of Texas at San Antonio), Murtuza Jadliwala (University of Texas at San Antonio)

The Tor network is the most prominent system for providing anonymous communication to web users, with a daily user base of 2 million users. However, since its inception, it has been constantly targeted by various traffic fingerprinting and correlation attacks aiming at deanonymizing its users. A critical requirement for these attacks is to attract as much user traffic to adversarial relays as possible, which is typically accomplished by means of bandwidth inflation attacks. This paper proposes a new inflation attack vector in Tor, referred to as MirageFlow, which enables inflation of measured bandwidth. The underlying attack technique exploits resource sharing among Tor relay nodes and employs a cluster of attacker-controlled relays with coordinated resource allocation within the cluster to deceive bandwidth measurers into believing that each relay node in the cluster possesses ample resources. We propose two attack variants, C-MirageFlow and D-MirageFlow, and test both versions in a private Tor test network. Our evaluation demonstrates that an attacker can inflate the measured bandwidth by a factor close to n using C-MirageFlow and nearly half n*N using D-MirageFlow, where n is the size of the cluster hosted on one server and N is the number of servers. Furthermore, our theoretical analysis reveals that gaining control over half of the Tor network's traffic can be achieved by employing just 10 dedicated servers with a cluster size of 109 relays running the MirageFlow attack, each with a bandwidth of 100MB/s. The problem is further exacerbated by the fact that Tor not only allows resource sharing but, according to recent reports, even promotes it.

View More Papers

TextGuard: Provable Defense against Backdoor Attacks on Text Classification

Hengzhi Pei (UIUC), Jinyuan Jia (UIUC, Penn State), Wenbo Guo (UC Berkeley, Purdue University), Bo Li (UIUC), Dawn Song (UC Berkeley)

Read More

SigmaDiff: Semantics-Aware Deep Graph Matching for Pseudocode Diffing

Lian Gao (University of California Riverside), Yu Qu (University of California Riverside), Sheng Yu (University of California, Riverside & Deepbits Technology Inc.), Yue Duan (Singapore Management University), Heng Yin (University of California, Riverside & Deepbits Technology Inc.)

Read More

TinyML meets IoBT against Sensor Hacking

Raushan Kumar Singh, Sudeepta Mishra (IIT Ropar)

Read More

Compromising Industrial Processes using Web-Based Programmable Logic Controller Malware

Ryan Pickren (Georgia Institute of Technology), Tohid Shekari (Georgia Institute of Technology), Saman Zonouz (Georgia Institute of Technology), Raheem Beyah (Georgia Institute of Technology)

Read More