- ]Mittal P, Olumofin F G, Troncoso C, PIR-Tor: Scalable Anonymous Communication Using Private Information Retrieval[C]//USENIX Security Symposium. 2011: 31.Google Scholar
- Clarke I, Sandberg O, Wiley B, Freenet: A distributed anonymous information storage and retrieval system[C]//Designing privacy enhancing technologies. Springer, Berlin, Heidelberg, 2001: 46-66.Google Scholar
- Zantout B, Haraty R. I2P data communication system[C]//Proceedings of ICN. 2011: 401-409.Google Scholar
- Junzhou L, Ming Y, Zhen L, Anonymous communication and darknet: A survey[J]. Journal of Computer Research and Development, 2019, 56(1): 103.Google Scholar
- Azzouna N B, Guillemin F. Analysis of ADSL traffic on an IP backbone link[C]//GLOBECOM'03. IEEE Global Telecommunications Conference (IEEE Cat. No. 03CH37489). IEEE, 2003, 7: 3742-3746.Google ScholarCross Ref
- Wang W, Zhu M, Wang J, End-to-end encrypted traffic classification with one-dimensional convolution neural networks[C]//2017 IEEE International Conference on Intelligence and Security Informatics (ISI). IEEE, 2017: 43-48.Google Scholar
- Cao Z, Xiong G, Zhao Y, A survey on encrypted traffic classification[C]//International Conference on Applications and Techniques in Information Security. Springer, Berlin, Heidelberg, 2014: 73-81.Google Scholar
- Pacheco F, Exposito E, Gineste M, Towards the deployment of machine learning solutions in network traffic classification: A systematic survey[J]. IEEE Communications Surveys & Tutorials, 2018, 21(2): 1988-2014.Google ScholarCross Ref
- Aminuddin M, Zaaba Z F, Singh M K M, A survey on tor encrypted traffic monitoring[J]. International Journal of Advanced Computer Science and Applications, 2018, 9(8): 113-120.Google ScholarCross Ref
- Liu Z, Wang R, Japkowicz N, Mobile app traffic flow feature extraction and selection for improving classification robustness[J]. Journal of Network and Computer Applications, 2019, 125: 190-208.Google ScholarCross Ref
- Tian G, Duan Z, Baumeister T, A traceback attack on freenet[J]. IEEE Transactions on Dependable and Secure Computing, 2015, 14(3): 294-307Google Scholar
- Habibi Lashkari A, Kaur G, Rahali A. DIDarknet: A Contemporary Approach to Detect and Characterize the Darknet Traffic using Deep Image Learning[C]//2020 the 10th International Conference on Communication and Network Security. 2020: 1-13.Google Scholar
- Salman O, Elhajj I H, Kayssi A, A review on machine learning–based approaches for internet traffic classification[J]. Annals of Telecommunications, 2020, 75(11): 673-710.Google ScholarCross Ref
- Rezaei S, Liu X. Deep learning for encrypted traffic classification: An overview[J]. IEEE communications magazine, 2019, 57(5): 76-81Google ScholarCross Ref
- Lashkari A H, Draper-Gil G, Mamun M S I, Characterization of tor traffic using time based features[C]//ICISSp. 2017: 253-262.Google Scholar
- Yao Z, Ge J, Wu Y, Meek-based tor traffic identification with hidden markov model[C]//2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, 2018: 335-340.Google Scholar
- Burschka S, Dupasquier B. Tranalyzer: Versatile high performance network traffic analyser[C]//2016 IEEE symposium series on computational intelligence (SSCI). IEEE, 2016: 1-8Google Scholar
- Rao Z, Niu W, Zhang X S, Tor anonymous traffic identification based on gravitational clustering[J]. Peer-to-Peer Networking and Applications, 2018, 11(3): 592-601.Google ScholarCross Ref
- Montieri A, Ciuonzo D, Aceto G, Anonymity services tor, i2p, jondonym: classifying in the dark (web)[J]. IEEE Transactions on Dependable and Secure Computing, 2018, 17(3): 662-675.Google ScholarCross Ref
- Shahbar K, Zincir-Heywood A N. How far can we push flow analysis to identify encrypted anonymity network traffic?[C]//NOMS 2018-2018 IEEE/IFIP Network Operations and Management Symposium. IEEE, 2018: 1-6.Google Scholar
- Kim M, Anpalagan A. Tor traffic classification from raw packet header using convolutional neural network[C]//2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII). IEEE, 2018: 187-190.Google Scholar
- Cai Z, Jiang B, Lu Z, isAnon: Flow-Based Anonymity Network Traffic Identification Using Extreme Gradient Boosting[C]//2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019: 1-8.Google Scholar
- Aminuddin M, Zaaba Z F, Singh M K M, A survey on tor encrypted traffic monitoring[J]. International Journal of Advanced Computer Science and Applications, 2018, 9(8): 113-120.Google ScholarCross Ref
- Rimmer V, Preuveneers D, Juarez M, Automated website fingerprinting through deep learning[J]. arXiv preprint arXiv:1708.06376, 2017Google Scholar
- Timpanaro J P, Isabelle C, Olivier F. Monitoring the I2P network[D]. Inria, 2011.Google Scholar
- Timpanaro J P, Chrisment I, Festor O. A bird's eye view on the I2P anonymous file-sharing environment[C]//International Conference on Network and System Security. Springer, Berlin, Heidelberg, 2012: 135-148.Google Scholar
- http://www.i2project.net/de/docs/transport/ntcpGoogle Scholar
- Timpanaro J P, Chrisment I, Festor O. Improving content availability in the I2P anonymous file-sharing environment[C]//International Symposium on Cyberspace Safety and Security. Springer, Berlin, Heidelberg, 2012: 77-92Google Scholar
- Timpanaro J P, Chrisment I, Festor O. Monitoring anonymous P2P file-sharing systems[C]//IEEE P2P 2013 Proceedings. IEEE, 2013: 1-2.Google Scholar
- Haraty R A, Assi M, Rahal I. A Systematic Review of Anonymous Communication Systems[C]//ICEIS (2). 2017: 211-220.Google Scholar
- Levine B N, Liberatore M, Lynn B, Statistical detection of downloaders in freenet[C]//CEUR Workshop Proceedings. 2017.Google Scholar
- Timpanaro J P, Chrisment I, Festor O. Group-based characterization for the i2p anonymous file-sharing environment[C]//2014 6th International Conference on New Technologies, Mobility and Security (NTMS). IEEE, 2014: 1-5.Google Scholar
- Timpanaro J P, Cholez T, Chrisment I, Evaluation of the anonymous I2P network's design choices against performance and security[C]//2015 International Conference on Information Systems Security and Privacy (ICISSP). IEEE, 2015: 1-10.Google Scholar
- Haraty R A, Assi M, Rahal I. A Systematic Review of Anonymous Communication Systems[C]//ICEIS (2). 2017: 211-220.Google Scholar
- Tian G, Duan Z, Baumeister T, A traceback attack on freenet[J]. IEEE Transactions on Dependable and Secure Computing, 2015, 14(3): 294-307.Google Scholar
- Nasr M, Bahramali A, Houmansadr A. Deepcorr: Strong flow correlation attacks on Tor using deep learning[C]//Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. 2018: 1962-1976.Google Scholar
- Zincir-Heywood K S A N. Anon17: Network Traffic Dataset of Anonymity Services[J].Google Scholar
- Habibi Lashkari A, Kaur G, Rahali A. DIDarknet: A Contemporary Approach to Detect and Characterize the Darknet Traffic using Deep Image Learning[C]//2020 the 10th International Conference on Communication and Network Security. 2020: 1-13.Google Scholar
Index Terms
- A Survey on Anonymous Communication Systems Traffic Identification and Classification
Recommendations
Learning for accurate classification of real-time traffic
CoNEXT '06: Proceedings of the 2006 ACM CoNEXT conferenceAccurate network traffic classification is an important task. We intend to develop an intelligent classification system by learning the types of service inside a network flow using machine learning techniques. Previous work used Bayesian methods for ...
A survey of techniques for internet traffic classification using machine learning
The research community has begun looking for IP traffic classification techniques that do not rely on `well known TCP or UDP port numbers, or interpreting the contents of packet payloads. New work is emerging on the use of statistical traffic ...
Machine Learned Real-Time Traffic Classifiers
IITA '08: Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application - Volume 03Network traffic classification plays an important role in various network activities. Due to the ineffectiveness of traditional port-based and payload-based methods, recent works proposed using machine learning methods to classify flows based on ...
Comments