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[1] D. Dubois, H. Prade. (1990), “Rough fuzzy sets and fuzzy rough sets,” International Journal of General Systems, vol. 17, 91–209, 1990

Sách, tạp chí

Tiêu đề:	Rough fuzzy sets and fuzzy rough sets
Tác giả:	D. Dubois, H. Prade
Năm:	1990

[7] R. Perdisci, D. Ariu, P. Fogla, G. Giacinto, W. Lee. (2009) "McPAD: A Multiple Classifier System for Accurate Payload-based Anomaly Detection." Computer Networks, Special Issue on Traffic Classification and Its Applications to Modern Networks, 5(6), pp. 864-881

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Tiêu đề:	McPAD: A Multiple Classifier System for Accurate Payload-based Anomaly Detection

[12] Richard Jensen (2010). Fuzzy-rough data mining with Weka. http://users.aber.ac.uk/rkj/Weka.pdf

Link

[2] B. Schőlkopf, J. Platt, J. Shawe-Taylor, A. J. Smola, and RC Williamson. Estimating the support of a high-dimensional distribution. Neural Computation, 13:1443–1471, 2001

Khác

[3] Mac Parthaláin, Neil & Jensen, Richard. (2009). Measures for Unsupervised Fuzzy-Rough Feature Selection. Int. J. Hybrid Intell. Syst. 7

Khác

[4] Jensen, Richard & Shen, Qiang. (2009). New Approaches to Fuzzy-Rough Feature Selection. IEEE Transactions on Fuzzy Systems. 17.10.1109/TFUZZ.2008.924209

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[5] M. Swarnkar and N. Hubballi. (2016). OCPAD: One class Naive Bayes classifier for payloadbased anomaly detection. Expert Systems with Applications 64,pp330-339

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[6] Pastrana S., Torrano-Gimenez C., Nguyen H.T., Orfila A. (2015) Anomalous Web Payload Detection: Evaluating the Resilience of 1-Grams Based Classifiers. In: Camacho D., Braubach L., Venticinque S., Badica C.(eds) Intelligent Distributed Computing VIII. Studies in Computational Intelligence, vol 570. Springer, Cham

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[8] K. Wang and S. Stolfo. Anomalous payload-based network intrusion detection. In Recent Advances in Intrusion Detection (RAID), 2004

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[9] K. Wang and S. Stolfo. Anomalous payload-based worm detection and signature generation. In Recent Advances in Intrusion Detection (RAID),2005

Khác

[10] Wang, K., Parekh, J., & Stolfo, S. (2006). Anagram: A content anomaly detector resistant to mimicry attack. In Raid’06: Proceedings of the 9th international conferenceon recent advances in intrusion detection (pp. 226–248)

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[11] R. Lippmann, J. W. Haines, D. J. Fried, J. Korba, and K. Das. (2000) The 1999 darpa off-line intrusion detection evaluation. Computer Networks, 34(4):579–595

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[13] I. S. Dhillon, S. Mallela, and R. Kumar. (2003) A divisive information- theoretic feature clustering algorithm for text classification. Journal of Machine Learning Research, 3:1265–1287

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[14] K. L. Ingham and H. Inoue (2007). Comparing anomaly detection techniques for HTTP. In Recent Advances in Intrusion Detection (RAID)

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[15] Kruegel, Christopher & Vigna, Giovanni. (2003). Anomaly Detection of Web-based Attacks. Proceedings of the ACM Conference on Computer and Communications Security. 10.1145/948109.948144

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[16] Reddy, R. & Yellasiri, Ramadevi & Sunitha (2016). Robust Data Model for Enhanced Anomaly Detection. 10.1007/978-981-10-0755-2_47

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[19] Sharafaldin, Iman & Habibi Lashkari, Arash & Ghorbani, Ali. (2018). Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. 108-116. 10.5220/0006639801080116

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