[1]
|
R. Perdisci, G. Gu and W. Lee, “Using an Ensemble of One-Class SVM Classifiers to Harden Payload-Based Anomaly Detection Systems,” Proceedings of the Sixth International Conference on Data Mining, Hong Kong, 18-22 December 2006, pp. 488-498.
doi:10.1109/ICDM.2006.165
|
[2]
|
R. Perdisci, “Statistical Pattern Recognition Techniques for Intrusion Detection in Computer Networks, Challenges and Solutions,” Ph.D. Thesis, University of Cagliari, Italy, 2006.
|
[3]
|
D. Anderson, T. Lunt, H. Javits and A. Tamaru, “Nides: Detecting Unusual Program Behavior Using the Statistical Component of the Next Generation Intrusion Detection Expert System,” Technical Report SRI-CSL-95-06, Computer Science Laboratory, SRI International, Menlo Park, 1995.
|
[4]
|
M. Mahoney, “Network Traffic Anomaly Detection Based on Packet Bytes,” ACM-SAC, Melbourne, 2003, pp. 346-350.
|
[5]
|
M. Mahoney and P. Chan, “Learning Non Stationary Models of Normal Network Traffic for Detecting Novel Attacks,” ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, July 2002, pp. 376-385.
|
[6]
|
K. Wang and S. Stolfo, “Anomalous Payload-Based Network Intrusion Detection,” Recent Advances in Intrusion Detection, Vol. 3224, 2004, pp. 203-222.
doi:10.1007/978-3-540-30143-1_11
|
[7]
|
K. Wang, “Network Payload-Based Anomaly Detection and Content-Based Alert Correlation,” Ph.D. Thesis, Columbia University, New York, 2006.
|
[8]
|
R. Perdisci, D. Ariu, P. Fogla, G. Giacinto and W. Lee, “McPAD: A Multiple Classifier System for Accurate Payload-Based Anomaly Detection,” Computer Networks, Special Issue on Traffic Classification and Its Applications to Modern Networks, Vol. 5, No. 6, 2009, pp. 864-881.
|
[9]
|
J. Gama, “Knowledge Discovery from Data Streams,” CRC Press, Boca Raton, pp. 7-9.
|
[10]
|
Z. Miller, W. Dietrick and W. Hu, “Anomalous Network Packet Detection Using Data Stream Mining,” Journal of Information Security, Vol. 2, No. 4, 2011, pp. 158-168.
doi:10.4236/jis.2011.24016
|
[11]
|
F. Cao, M. Ester, W. Quan and A. Zhou, “Density-Based Clustering over an Evolving Data Stream with Noise,” 2006 SIAM Conference on Data Mining, Bethesda, 20-22 April 2006.
|
[12]
|
C. Bohm, K. Kailing, H. Kriegel and P. Kroger, “Density Connected Clustering with Local Subspace Preferences,” Proceedings of the Fourth IEEE International Conference on Data Mining, Brighton, 1-4 November 2004, pp. 27-34.
|
[13]
|
M. Ester, H. Kriegel, J. Sander and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” International Conference on Knowledge Discovery in Databases and Data Mining (KDD-96), Portland, August 1996, pp. 226-231.
|
[14]
|
M. Ankerst, M. Breunig, H. Kriegel and J. Sander, “OPTICS: Ordering Points to Identify the Clustering Structure,” SIGMOD, Philadelphia, 1999, pp. 49-60.
|
[15]
|
H. Kriegel, P. Kroger, I. Ntoutsi and A. Zimek, “Towards Subspace Clustering on Dynamic Data: An Incremental Version of PreDeCon,” Proceedings of First International Workshop on Novel Data Stream Pattern Mining Techniques, Washington DC, 2010, pp. 31-38.
doi:10.1145/1833280.1833285
|