T. Lane and C. E. Brodley, Temporal sequence learning and data reduction for anomaly detection, ACM Transactions on Information and System Security, vol.2, issue.3, pp.295-331, 1999.
DOI : 10.1145/322510.322526
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.158.7469

D. Sculley and G. M. Wachman, Relaxed online SVMs for spam filtering, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '07, pp.415-422, 2007.
DOI : 10.1145/1277741.1277813
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.298.2346

M. Eirinaki, M. Vazirgiannis, and D. Kapogiannis, Web path recommendations based on page ranking and Markov models, Proceedings of the seventh ACM international workshop on Web information and data management , WIDM '05, pp.2-9, 2005.
DOI : 10.1145/1097047.1097050
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.467.4201

F. Rousseau and M. Vazirgiannis, Graph-of-word and TW-IDF, Proceedings of the 22nd ACM international conference on Conference on information & knowledge management, CIKM '13, pp.59-68, 2013.
DOI : 10.1145/2505515.2505671

C. Warrender, S. Forrest, and B. Pearlmutter, Detecting intrusions using system calls: alternative data models, Proceedings of the 1999 IEEE Symposium on Security and Privacy (Cat. No.99CB36344), pp.133-145, 1999.
DOI : 10.1109/SECPRI.1999.766910

S. A. Hofmeyr, S. Forrest, and A. Somayaji, Intrusion detection using sequences of system calls, Journal of Computer Security, vol.10, issue.1, pp.151-180, 1998.
DOI : 10.1016/0167-4048(93)90110-Q

S. Forrest, S. A. Hofmeyr, A. Somayaji, and T. A. Longstaff, A sense of self for Unix processes, Proceedings 1996 IEEE Symposium on Security and Privacy, pp.120-128, 1996.
DOI : 10.1109/SECPRI.1996.502675

L. Portnoy, E. Eskin, and S. Stolfo, Intrusion detection with unlabeled data using clustering, Proceedings of ACM CSS Workshop on Data Mining Applied to Security (DMSA-2001. Citeseer, 2001.

A. Scherbina and S. Kuznetsov, Clustering of Web Sessions Using Levenshtein Metric, Industrial Conference on Data Mining, pp.127-133, 2004.
DOI : 10.1109/34.85677

R. Blanco and C. Lioma, Graph-based term weighting for information retrieval, Information Retrieval, vol.393, issue.3, pp.54-92, 2012.
DOI : 10.1038/30918
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.348.6172

E. S. Ristad and P. N. Yianilos, Learning string-edit distance, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.5, pp.522-532, 1998.
DOI : 10.1109/34.682181
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.269.3581

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Data Mining, Inference and Prediction, 2009.

T. Fawcett, An introduction to ROC analysis, Pattern Recognition Letters, vol.27, issue.8, pp.861-874, 2006.
DOI : 10.1016/j.patrec.2005.10.010

B. Cao, Y. Li, and J. Yin, Measuring Similarity between Graphs Based on the Levenshtein Distance, Applied Mathematics & Information Sciences, vol.7, issue.1L, pp.169-175, 2013.
DOI : 10.12785/amis/071L24