• 1
    Crotti M, Dusi M, Gringoli F, Salgarelli L. Traffic classification through simple statistical fingerprinting. SIGCOMM Computer Communication Review 2007; 37(1):516.
  • 2
    Dainotti A, Gargiulo F, Kuncheva LI, Pescape A, Sansone C. Identification of traffic flows hiding behind TCP port 80. Proceedings of the IEEE ICC11, 2010.
  • 3
    Dainotti A, Pescape A, Sansone C. Early classification of network traffic through multi-classification. In Proceedings of the Third international conference on Traffic monitoring and analysis.Springer-Verlag, 2011; 122135.
  • 4
    Sun Q, Simon DR, Wang YM, Russell W, Padmanabhan VN, Qiu L. Statistical identification of encrypted web browsing traffic. Proceedings of the IEEE Symposium on ecurity and Privacy, 2002.
  • 5
    Liberatore L, Levine BN. Inferring the source of encrypted HTTP connections. Proceedings of the 13th ACM conference on Computer and communications security, 2006; 255263.
  • 6
    Wright C, Coull S, Monrose F. Traffic morphing: An efficient defense against statistical traffic analysis. Proceedings of the Network and Distributed System Security Symposium, 2009; 375382.
  • 7
    Witten I, Frank E. Data Mining: Practical Machine Learning Tools and Techniques (2nd edn). Morgan Kaufmann: Boston, 2005.
  • 8
    The unibs anonymized 2009 internet traces,, Date of access: Mar. 3, 2010.
  • 9
    Dusi M, Este A, Gringoli F, Salgarelli L. Using GMM and SVM-based techniques for the classification of SSH-encrypted traffic. Proceedings of the IEEE ICC09, 2009; 702707.
  • 10
    Archibald R, Liu Y, Corbett C, Ghosal D. Disambiguating HTTP classifying web applications. IEEE International Workshop on TRaffic Analysis and Classification (TRAC11), Istanbul, Turkey, 2011; 18081813.
  • 11
    Jaber M, Cascella RG, Barakat C. Can we trust the inter-packet time for traffic classification? Proceedings of the IEEE ICC11, 2011.
  • 12
    Yu S, Thapngam T, Wei S, Zhou W. Efficient web browsing with perfect anonymity using page prefetching. In Algorithms and Architectures for Parallel Processing, ser. Lecture Notes in Computer Science, Hsu CH, Yang L, Park J, Yeo SS, Eds.Springer: Berlin / Heidelberg, 2010, 6081, 112.
  • 13
    Celik Z, Raghuram J, Kesidis G, Miller DJ. Salting public traces with attack traffic to test flow classifiers. 4th workshop on Cyber Security Experimentation and Test, San Francisco, CA, Aug 8, 2011.
  • 14
    Schear N, Borisov N.. Preventing SSL traffic analysis with realistic cover traffic (extended abstract). 16th ACM Conference on Computer and Communications Security, CCS09 Poster Session, Chicago, IL, November 2009.
  • 15
    Valenti S, Rossi D. Fine-grained behavioral classification in the core: the issue of flow sampling. IEEE International Workshop on TRaffic Analysis and Classification (TRAC11), Istanbul, Turkey; 10281032, 2011.
  • 16
    Iacovazzi A, Baiocchi A. Padding and fragmentation for masking packet length statistics. TMA12, 2012; 8588.
  • 17
    Fu X, Graham B, Bettati R, Zhao W. On effectiveness of link padding for statistical traffic analysis attacks. Proceedings of the 23rd International Conference on Distributed Computing Systems, 2003.
  • 18
    Iacovazzi A, Baiocchi A. Optimum packet length masking. Teletraffic Congress (ITC), 22nd International, sept. 2010; 18.
  • 19
    Gringoli F, Salgarelli L, Dusi M, Cascarano N, Risso F, Claffy KC. GT: picking up the truth from the ground for internet traffic. SIGCOMM Comput. Commun. Rev. 2009; 39(5):1218.
  • 20
    Qu B, Zhang Z, Guo L, Meng D. On accuracy of early traffic classification. Proceedings of the 7th IEEE International Conference on Networking, Architecture, and Storage, 2012; 348354.
  • 21
    Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: an update. SIGKDD Explorations Newsletter 2009; 11(1):1018.
  • 22
    Quinlan JR. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Francisco, CA, USA, 1993.
  • 23
    Cortes C, Vapnik V. Support-Vector Networks. Machine Learning 1995; 20:273297.
  • 24
    Bishop M. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag: New York, 2006.
  • 25
    Kotsiantis SB. Supervised machine learning: a review of classification techniques. Informatica 2007; 31:24968.
  • 26
    Hsu CW, Chang C-C, Lin CJ. A practical guide to support vector classification., 2003.