Eleazar Eskin, Matthew Miller, Zhi-Da Zhong, George Yi, Wei-Ang Lee, Salvatore Stolfo  Home/Search
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Abstract: In this paper, we present adaptive model generation, a method for automatically building detection models for data-mining based intrusion detection systems. Using the same data collected by intrusion detection sensors, adaptive model generation builds detection models on the fly. This significantly reduces the deployment cost of an intrusion detection system because it does not require building a training set. We present a real time system architecture and efficient implementation of automatic... (Update)
Context of citations to this paper: More
...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...
...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...
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BibTeX entry: (Update)
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. http://citeseer.ist.psu.edu/eskin00adaptive.html More
@inproceedings{ eskin00adaptive,
author = "E. Eskin and M. Miller and Z. Zhong and G. Yi and W. Lee and S. Stolfo",
title = "Adaptive model generation for intrusion detection",
booktitle = "Proceedings of the ACMCCS Workshop
on Intrusion Detection and Prevention, Athens, Greece, 2000.",
year = "2000",
url = "citeseer.ist.psu.edu/eskin00adaptive.html" }
Citations (may not include all citations):142 A sense of self for unix processes - Forrest, Hofmeyr et al. - 1996
133 IEEE Transactions on Software Engineering (context) - Denning, detection - 1987
86 JAM: Java agent for meta learning over distributed databases - Stolfo, Prodromidis et al. - 1997
84 Data mining approaches for intrusion detection - Lee, Stolfo - 1998
70 The case against accuracy estimation for comparing induction.. - Provost, Fawcett et al. - 1998
62 The nides statistical component: Description and justificati.. (context) - Javitz, Valdes - 1993
60 Detecting intrusions using system calls: alternative data mo.. - Warrender, Forrest et al. - 1999
40 Temporal sequence learning and data reduction for anomaly de.. - Lane, Brodley - 1998
40 Temporal sequence learning and data reduction for anomaly de.. - Lane, Brodley - 1999
28 Adaptive real-time anomaly detection using inductively gener.. (context) - Teng, Chen et al. - 1990
27 A study in using neural networks for anomaly and misuse dete.. (context) - Ghosh, Schwartzbard - 1999
25 Sequence matching and learning in anomaly detection for comp.. - Lane, Brodley - 1997
22 Anomaly detection over noisy data using learned probability .. - Eskin - 2000
15 Learning patterns from unix processes execution traces for i.. (context) - Lee, Stolfo et al. - 1997
11 Intrusion detect using sequences of system calls (context) - Hofmeyr, Forrest et al. - 1998
8 The common intrusion detection framework (context) - Staniford-Chen, Tung et al. - 1998
7 Data mining in work flow environments: Experiences in intrus.. (context) - Lee, Stolfo et al. - 1999
4 Intrusion detection exchange format (context) - Task - 2000
4 A statistically base system for prioritizing information exp.. (context) - Helman, Bhangoo - 1997
3 and experiences in automated audit analysis (context) - Sobirey, Richter et al. - 1996
1 DARPA intrusion detection evaluation In http://www (context) - Labs - 1999
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