Lee, W., Stolfo, S., and Mok, K. (1999b). A Data Mining Framework for Adaptive Intrusion Detection. Arti cial Intelligence Review. Home/Search Document Details and Download
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Collective Data Mining: A New Perspective Toward.. - Kargupta, Byung-Hoon, al (1999) (20 citations) (Correct)
....algorithms may be employed to learn the local concepts, and the meta level learning may be applied recursively, producing a hierarchy of meta classi ers. The JAM system [36] is a meta learning based distributed data mining framework. It has been used for fraud detection in the banking domain [27]. The knowledge probing approach is reported in [14] This technique is similar to meta learning. However this approach is particularly designed for inducing descriptive data model from the predictions of black box classi ers learned in a distributed environment. The Distributed cooperative ....
W. Lee, S. Stolfo, and Kui Mok. A data mining framework for adaptive intrusion detection. In Proceedings of the 1999 IEEE Symposium on Security and Privacy, page Not available. IEEE Press, 1999.
Collective Data Mining: A New Perspective Toward Distributed.. - Kargupta, al (1999) (20 citations) (Correct)
....This section briefly reviews some of these efforts and other related works. The meta learning (Chan Stolfo, 1993b; Chan Stolfo, 1993a; Chan Stolfo, 1998) based JAM system (Stolfo et al. 1997) is a distributed data mining framework used primarily for fraud detection in the banking domain (Lee, Stolfo, Mok, 1999). This system develops patterns of fraudulent activity by mining the individual databases of the various financial institutions, and then combining the patterns to form an overall pattern. The JAM system is a java based multi agent system in which different data mining agents are allowed to have ....
Lee, W., Stolfo, S., & Mok, K. (1999). A data mining framework for adaptive intrusion detection.
Distributed Multivariate Regression Using Wavelet-based.. - Hershberger, Kargupta (1999) (2 citations) (Correct)
....may be employed to learn the local concepts, and the meta level learning may be applied recursively, producing a hierarchy of meta classifiers. The JAM system (Stolfo et al. 1997) is a meta learning base distributed data mining framework has been used for fraud detection in the banking domain (Lee, Stolfo, Mok, 1999). Collective data mining (Kargupta, Johnson, Riva Sanseverino, Park, Silvestre, Hershberger, 1998; Kargupta, Park, Hershberger, Johnson, 1999) address the issues associated with mining heterogeneous data sites. At the foundation of CDM is the observation that any function may be represented in ....
Lee, W., Stolfo, S., & Mok, K. (1999). A data mining framework for adaptive intrusion detection. To appear in the Proceedings of the 1999 IEEE Symposium on Security and Privacy, IEEE Computer Society Press.
Intrusion Detection: A Bibliography - Mé, Michel (2001) (Correct)
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Lee, W., Stolfo, S., and Mok, K. (1999b). A Data Mining Framework for Adaptive Intrusion Detection. Arti cial Intelligence Review.
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