Network Flow Watermarking Attack on Low-Latency Anonymous Communication Systems

Wang, Xinyuan; Chen, Shiping; Jajodia, Sushil

doi:10.1109/sp.2007.30

Cited by 173 publications

(183 citation statements)

References 30 publications

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“…Linking network flows is an important problem in different applications, including stepping stones detection [13,28] and compromising anonymity [22,23]. For instance, it is widely known that attackers can de-anonymize a low-latency anonymity system by linking its egress and ingress flows.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, it is widely known that attackers can de-anonymize a low-latency anonymity system by linking its egress and ingress flows. Since network flows are commonly encrypted in such applications, linking flows is feasible only through correlation of communication patterns such as packet timings, packet counts, and packet sizes [13,22,23]; this is known as traffic analysis.…”

Section: Introductionmentioning

confidence: 99%

“…To illustrate this better, consider the following application scenario. Previous research [22,23] have designed flow watermarking systems in order to attack anonymity systems by linking their egress and ingress flows. Such watermarks, however, can only conduct a targeted attack in this scenario, i.e., they can identify the egress flows corresponding to a "single" target, ingress flow.…”

Section: Introductionmentioning

confidence: 99%

“…Such watermarks, however, can only conduct a targeted attack in this scenario, i.e., they can identify the egress flows corresponding to a "single" target, ingress flow. On the other hand, one might need to perform a non-targeted attack in this case, i.e., to simultaneously identify the egress flows corresponding to "many" ingress flows observed; this can not be accomplished through flow watermarking [10,13,19,23,27] as they do not have efficient mechanisms to confidently distinguish among various watermark tags. Performing such a non-targeted attack requires a flow fingerprinting scheme that embeds various tags on different ingress flows, and that is reliably able to extract them from the egress flows.…”

Section: Introductionmentioning

confidence: 99%

“…The literature on active traffic analysis has mainly studied the flow watermarking problem [10,13,19,23,27] while entirely ignoring flow fingerprinting. This comes as a surprise given that, in several important applications of traffic analysis, flow fingerprinting is indeed the solution to the problem, not watermarking (see Section 2).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

The Need for Flow Fingerprints to Link Correlated Network Flows

Houmansadr

Borisov

2013

Privacy Enhancing Technologies

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Abstract. Linking network flows is an important problem in the detection of stepping stone attacks as well as in compromising anonymity systems. Traffic analysis is an effective tool for linking flows, which works by correlating their communication patterns, e.g., their packet timings. To improve scalability and performance of this process, recent proposals suggest to perform traffic analysis in an active manner by injecting invisible tags into the traffic patterns of network flows; this approach is commonly known as flow watermarking. In this paper, we study an under-explored type of active traffic analysis that we call it flow fingerprinting. Information theoretically, flow watermarking aims at conveying a single bit of information whereas flow fingerprinting tries to reliably send multiple bits of information, hence it is a more challenging problem. Such additional bits help a fingerprinter deliver extra information in addition to the existence of the tag, such as the network origin of the flow and the identity of the fingerprinting entity. In this paper, we introduce and formulate the flow fingerprinting problem and contrast its application scenarios from that of the well-studied flow watermarking. We suggest the use of coding theory to build fingerprinting schemes based on the existing watermarks. In particular, we design a non-blind fingerprint, Fancy, and evaluate its performance. We show that Fancy can reliably fingerprint millions of network flows by tagging only as few as tens of packets from each flow.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Need for Flow Fingerprints to Link Correlated Network Flows

Houmansadr

Borisov

2013

Privacy Enhancing Technologies

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Network Flow Watermarking

Mazurczyk¹,

Wendzel²,

Zander³

et al. 2016

Information Hiding in Communication Networks: Fundamentals, Mechanisms, Applications, and Countermeasures

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Security and Privacy in the Internet of Things

2021

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The Internet of Things (IoT) is an emerging paradigm that seamlessly integrates electronic devices with sensing and computing capability into the Internet to achieve intelligent processing and optimized controlling. In a connected world built through IoT, where interconnected devices are extending to every facet of our lives, including our homes, offices, utility infrastructures and even our bodies, we are able to do things in a way that we never before imagined. However, as IoT redefines the possibilities in environment, society and economy, creating tremendous benefits, significant security and privacy concerns arise such as personal information confidentiality, and secure communication and computation. Theoretically, when everything is connected, everything is at risk. The ubiquity of connected things gives adversaries more attack vectors and more possibilities, and thus more catastrophic consequences by cybercrimes. Therefore, it is very critical to move fast to address these rising security and privacy concerns in IoT systems before severe disasters happen. In this dissertation, we mainly address the challenges in two domains: (1) how to protect IoT devices against cyberattacks;(2) how to protect sensitive data during storage, dissemination and utilization for IoT applications.In this section, I would like to express my gratitude to my advisor, my colleagues, my committee members and my family for their guidance, encouragement and support along the road of my PhD study.First of all, I would like to express my deepest gratitude to my advisor, Prof. Fengjun Li, for her tremendous support, guidance, encouragement, patience and contributions to the ideas in this dissertation. I could not have done this without her help. When I began my PhD, she led me to security field and help me grow from an undergraduate to a PhD. I will never forget her patience on guiding me to identify research questions, tackle them and write a research paper. Her patience, enthusiasm, knowledge and insights have been a great source of inspiration and will also benefit my whole life. I will always cherish the time we spent working together.

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Network Flow Watermarking Attack on Low-Latency Anonymous Communication Systems

Abstract: Abstract

Cited by 173 publications

References 30 publications

The Need for Flow Fingerprints to Link Correlated Network Flows

The Need for Flow Fingerprints to Link Correlated Network Flows

Network Flow Watermarking

Security and Privacy in the Internet of Things

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