E. Eskin, M. Miller, Z. Zhong, G. Yi, W. Lee, S. Stolfo. Adaptive model generation of intrusion detection. In Proceedings of the ACM CCS Workshop on Intrusion Detection and Prevention. Athens, Greece, 2000. Home/Search Document Details and Download
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An Empirical Analysis of NATE - Network Analysis of.. - Taylor, Alves-Foss (2002) (2 citations) (Correct)
....derived rule set for classifying intrusive activity. The technique also uses network traffic header information but it is not clear how efficient the method would be under actual operation. Another recent technique utilizes conditional probability to determine the likelihood of anomalous behavior [8]. The method works by computing the likelihood of the nth call given n 1 previous calls. Yet, another recent statistical method analyzes system calls in privileged processes with discriminant analysis, a multivariate statistical technique [2] This technique appears to be quite efficient utilizing ....
....intervals and manipulated to suit individual research needs. Another benefit from using a public, widely distributed data set is it represents a standard against which IDS s can be compared. A number of studies based their research on this data set which in theory allows them to compare results [8,9,15]. Yet, given that problems were identified in the Lincoln data [18] relying on it as a data source may not be desirable for ID development. The inherent danger in relying on a simulated data set for any type of research is it may not be representative of the real word. For us, the real data ....
E. Eskin, M. Miller, Z. Zhong, G. Yi, W. Lee, S. Stolfo. Adaptive model generation of intrusion detection. In Proceedings of the ACM CCS Workshop on Intrusion Detection and Prevention. Athens, Greece, 2000.
Real Time Data Mining-based Intrusion Detection - Lee, Stolfo, Chan, Eskin.. (2001) (2 citations) Self-citation (Eskin Miller Stolfo) (Correct)
....or the components can be in different networks, in which case, they can also participate in the collaboration with other IDSs in the Internet. In the following sections we describe the components depicted in Figure 2 in more detail. A complete description of the system architecture is given in [6]. 5.1 Sensors Sensors observe raw data on a monitored system and compute features for use in model evaluation. Sensors insulate the rest of the IDS from the specific low level properties of the target system being monitored. This is done by having all of the sensors implement a Basic Auditing ....
E. Eskin, M. Miller, Z.-D. Zhong, G. Yi, W.-A. Lee, and S. Stolfo. Adaptive model generation for intrusion detection. In Proceedings of the ACMCCS Workshop on Intrusion Detection and Prevention, Athens, Greece, 2000.
Intrusion Detection: A Bibliography - Mé, Michel (2001) (Correct)
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Eskin, E., Miller, M., Zhong, Z.-D., Yi, G., Lee, W.-A., and Stolfo, S. (2000). Adaptive Model Generation for Intrusion Detection Systems. In Proceedings of the ACMCCS Workshop on Intrusion Detection and Prevention.
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