The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Traffic Analysis Attacks on Tor: A Survey
Abstract
The Tor anonymity network is one of the most popular and widely used tools to protect the privacy of online users. Tor provides defenses against multiple adversarial activities aiming to identify or trace the users. Traffic analysis is a very strong tool that can be used for internet surveillance. Traffic analysis attacks against Tor's anonymity network has been known as an open question in research. Moreover, the low-latency feature Tor tries to provide to its users imposes limitations in defending against traffic analysis attacks. In our study, we examine traffic analysis attacks from the perspective of the adopted adversary model and how much it fits within Tor's threat model. The purpose of this study is to evaluate how practical these attacks are on real-time Tor network.
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In Prime MotorX's network layer, insufficient data and transport encryption is exploited by eavesdropping attacks on unencrypted sensor data.
“In this scenario, when the data is unencrypted, the attacker exploits insufficient data and transport encryption as a vulnerability and launches an Eavesdropping [70], [71], with the target being Unencrypted Data.”
From A Critical Overview of Industrial Internet of Things Security and Privacy Issues Using a Layer-Based Hacking Scenario (2023) · cites this paper as [71]
Citations
Cites
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Low-Cost Traffic Analysis of Tor
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The Sniper Attack: Anonymously Deanonymizing and Disabling the Tor Network
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Inferring Application Type Information from Tor Encrypted Traffic
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Flagged first, then by citations
Research works printed but not linked: 4
- 0, Tor Onion Service Protocol
- 0, Configuring Hidden Services for Tor
- perry, 2011, Experimental defense for website traffic fingerprinting
- arp, 0, Torben: A Practical Side-Channel Attack for Deanonymizing Tor Communication Categories and Subject Descriptors, Proceedings of the 10th ACM Symposium on Information Computer and Communications Security, 597
Cited by
Sorted by citations
Machine-Learning-Based Darknet Traffic Detection System for IoT Applications
Abu Al-Haija, Q., Krichen, M., & Abu Elhaija, W. (2022) · Electronics · doi:10.3390/electronics11040556 · Cited by 120
Eyeleko, A. H., & Feng, T. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3308195 · Cited by 38
“On the other hand, when the data is encrypted, the attacker launches a Traffic Analysis attack [71], utilizing Wireshark [72], and a Replay attack [73], with the target being the encrypted data.”
Method
Metadata
- Authors
- Basyoni, L., Fetais, N., Erbad, A., Mohamed, A., & Guizani, M.
- Affiliations
- Qatar University (QA)
- Venue
- 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT)
- Publisher
- IEEE
- Published
- 2020-02
- Pages
- 183-188
- OpenAlex
- W3025003062
- Cited by
- 54
- References
- 26
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 14 · 2023: 13 · 2022: 7 · 2021: 8 · 2020: 1
- FWCI
- 2.77
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) (publishedVersion)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- Tor, traffic analysis attacks, anonymity networks, online privacy, adversary models, threat model, online surveillance
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