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Characterization of Tor Traffic using Time based Features
Open accessAbstract
: Traffic classification has been the topic of many research efforts, but the quick evolution of Internet services and the pervasive use of encryption makes it an open challenge. Encryption is essential in protecting the privacy of Internet users, a key technology used in the different privacy enhancing tools that have appeared in the recent years. Tor is one of the most popular of them, it decouples the sender from the receiver by encrypting the traffic between them, and routing it through a distributed network of servers. In this paper, we present a time analysis on Tor traffic flows, captured between the client and the entry node. We define two scenarios, one to detect Tor traffic flows and the other to detect the application type: Browsing, Chat, Streaming, Mail, Voip, P2P or File Transfer. In addition, with this paper we publish the Tor labelled dataset we generated and used to test our classifiers.
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Cited by
Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A. (2018) · Proceedings of the 4th International Conference on Information Systems Security and Privacy · doi:10.5220/0006639801080116 · Cited by 5,318
“We begin to extract the 80 traffic features from the dataset using CICFlowMeter (CICFlowMeter, 2017),(Habibi Lashkari et al., 2017).”
MethodSelf-citationDeveloping Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy
Sharafaldin, I., Lashkari, A. H., Hakak, S., & Ghorbani, A. A. (2019) · 2019 International Carnahan Conference on Security Technology (ICCST) · doi:10.1109/ccst.2019.8888419 · Cited by 1,275
Self-citationNetwork Intrusion Detection for IoT Security Based on Learning Techniques
Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C., & Faruki, P. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2896380 · Cited by 940
Lin, X., Xiong, G., Gou, G., Li, Z., Shi, J., & Yu, J. (2022) · Proceedings of the ACM Web Conference 2022 · doi:10.1145/3485447.3512217 · Cited by 599
A Scheme for Generating a Dataset for Anomalous Activity Detection in IoT Networks
Ullah, I., & Mahmoud, Q. H. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-47358-7_52 · Cited by 319
Metadata
- Authors
- Habibi Lashkari, A., Draper Gil, G., Mamun, M. S. I., & Ghorbani, A. A.
- Venue
- Proceedings of the 3rd International Conference on Information Systems Security and Privacy
- Published
- 2017
- Pages
- 253-262
- OpenAlex
- W2591712613
- Cited by
- 799
- References
- 0
- FWCI
- 16.01
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Habibi Lashkari, A., Draper Gil, G., Mamun, M. S. I., & Ghorbani, A. A. (2017). Characterization of Tor Traffic using Time based Features. Proceedings of the 3rd International Conference on Information Systems Security and Privacy. https://doi.org/10.5220/0006105602530262
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