Characterization of Tor Traffic using Time based Features
By Arash Habibi Lashkari, G. Draper-Gil, M. Mamun, Ali Akbar Ghorbani. International Conference on Information Systems Security and Privacy, 2017
887 citations recorded by the source corpus.
: 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.
Subfields: Control and Systems Engineering, Electrical and Electronic Engineering, Hardware and Architecture
View Paper: https://www.semanticscholar.org/paper/d76f32eb3af1a163c0fde624e9fc229671ca75b6
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Iman Sharafaldin
- André Costa Drummond
- Paulo Angelo Alves Resende
- Ali Akbar Ghorbani
- Arash Habibi Lashkari
- Mohamed Amine Ferrag
In Electrical and Electronic Engineering
- Iman Sharafaldin
- André Costa Drummond
- Paulo Angelo Alves Resende
- Ali Akbar Ghorbani
- Arash Habibi Lashkari
- Helge Janicke
- Mohamed Amine Ferrag
- Leandros A. Maglaras
In Hardware and Architecture
- Iman Sharafaldin
- André Costa Drummond
- Paulo Angelo Alves Resende
- Ali Akbar Ghorbani
- Arash Habibi Lashkari
Citations in the corpus
- Network Intrusion Detection for IoT Security Based on Learning Techniques (2019, methodology): “CICFlowMeter [93] with labeled flows based on timestamp, initial and final IP, ports, protocols and attacks.”
- Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization (2018, methodology): “We begin to extract the 80 traffic features from the dataset using CICFlowMeter (CICFlowMeter, 2017),(Habibi Lashkari et al., 2017).”
- Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy (2019, methodology): “on the Canadian Institute for Cybersecurity website [10].”
- A Survey of Random Forest Based Methods for Intrusion Detection Systems (2018, methodology): “Habibi Lashkari et al. (2017) propose an approach based on a Random Forest classifier to identify the used application (browsing, chat, streaming, mail, VoIP, P2P, or file transfer) inside an encrypted Tor communication.”
- Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study (2020, background): “[108], which contains 8 types of traffic (VOIP, chat, audio-streaming, video-streaming, mail, P2P, browsing, and File Transfer) from more than 18 representative applications (e.”