Y. Bengio, O. Delalleau, N. L. Roux, J. F. Paiement, P. Vincent et al., Feature Extraction, chap. Spectral Dimensionality Reduction, Studies in Fuzziness and Soft Computing, pp.519-550, 2006.

V. Chandola, A. Banerjee, and V. Kumar, Anomaly detection, ACM Computing Surveys, vol.41, issue.3, pp.1-58, 2009.
DOI : 10.1145/1541880.1541882

C. C. Chang and C. J. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, 2001.
DOI : 10.1145/1961189.1961199

F. R. Chung, Spectral Graph Theory, 1997.
DOI : 10.1090/cbms/092

R. R. Coifman, S. Lafon, A. B. Lee, M. Maggioni, B. Nadler et al., Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps, Proceedings of the National Academy of Sciences of the United States of America, p.7426, 2005.
DOI : 10.1073/pnas.0500896102

R. R. Coifman and S. Lafon, Diffusion maps, Applied and Computational Harmonic Analysis, vol.21, issue.1, pp.5-30, 2006.
DOI : 10.1016/j.acha.2006.04.006

M. Damashek, Gauging Similarity with n-Grams: Language-Independent Categorization of Text, Science, vol.17, issue.2, p.843, 1995.
DOI : 10.1016/0306-4573(81)90044-3

G. David, Anomaly Detection and Classification via Diffusion Processes in Hyper- Networks, 2009.

J. Han, M. Kamber, and ¨. O. Izmirli, Data Mining, 10th International Society for Music Information Retrieval Conference, 2006.
DOI : 10.1145/233269.233324

URL : https://hal.archives-ouvertes.fr/hal-01534761

R. Kannan, S. Vempala, and A. Vetta, On clusterings, Journal of the ACM, vol.51, issue.3, pp.497-515, 2004.
DOI : 10.1145/990308.990313

Y. Keller, R. Coifman, S. Lafon, and S. Zucker, Audio-Visual Group Recognition Using Diffusion Maps, IEEE Transactions on Signal Processing, vol.58, issue.1, pp.403-413, 2010.
DOI : 10.1109/TSP.2009.2030861

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.472.2817

U. Von-luxburg, A tutorial on spectral clustering, Statistics and Computing, vol.21, issue.1, pp.395-416, 2007.
DOI : 10.1017/CBO9780511810633

M. Meila and J. Shi, Learning segmentation by random walks, pp.873-879, 2000.

S. Mukkamala and A. Sung, A comparative study of techniques for intrusion detection, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence, 2003.
DOI : 10.1109/TAI.2003.1250243

B. Nadler, S. Lafon, R. Coifman, I. G. Kevrekidis, T. J. Barth et al., Diffusion Maps - a Probabilistic Interpretation for Spectral Embedding and Clustering Algorithms, Principal Manifolds for Data Visualization and Dimension Reduction, pp.238-260, 2008.
DOI : 10.1007/978-3-540-73750-6_10

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.3275

A. Y. Ng, M. I. Jordan, and Y. Weiss, On spectral clustering: Analysis and an algorithm, Advances in Neural Information Processing Systems 14, pp.849-856, 2001.

A. Nguyen-tuong, S. Guarnieri, D. Greene, J. Shirley, and D. Evans, Automatically Hardening Web Applications Using Precise Tainting, Security and Privacy in the Age of Ubiquitous Computing, IFIP Advances in Information and Communication Technology, pp.295-307, 2005.
DOI : 10.1007/0-387-25660-1_20

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.1565

A. Patcha and J. Park, An overview of anomaly detection techniques: Existing solutions and latest technological trends, Computer Networks, vol.51, issue.12, pp.3448-3470, 2007.
DOI : 10.1016/j.comnet.2007.02.001

M. Ramadas, S. Ostermann, and B. Tjaden, Detecting anomalous network traffic with self-organizing maps Recent Advances in Intrusion Detection, pp.36-54, 2003.
DOI : 10.1007/978-3-540-45248-5_3

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.66.837

A. Schclar, A. Averbuch, N. Rabin, V. Zheludev, and K. Hochman, A diffusion framework for detection of moving vehicles, Digital Signal Processing, vol.20, issue.1, pp.111-122, 2010.
DOI : 10.1016/j.dsp.2009.02.002

J. Shi and J. Malik, Normalized cuts and image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.22, issue.8, pp.888-905, 2000.

Q. Tran, H. Duan, and X. Li, One-class support vector machine for anomaly network traffic detection, China Education and Research Network Main Building, p.310, 2004.

Q. A. Tran, Q. Zhang, and X. Li, Evolving training model method for one-class svm, IEEE International Conference on, pp.2388-2393, 2003.

J. Turkka, T. Ristaniemi, G. David, and A. Averbuch, Anomaly detection framework for tracing problems in radio networks, Proc. to ICN, p.2011, 2011.