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A Framework for Constructing Features and Models for Intrusion Detection Systems (2000)  (Make Corrections)  (18 citations)
Wenke Lee, Salvatore J. Stolfo
Information and System Security



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Abstract: This paper describes a novel framework, MADAM ID, for Mining Audit Data for Automated Models for Intrusion Detection. This framework uses data mining algorithms to compute activity patterns from system audit data and extracts predictive features from the patterns. It then applies machine learning algorithms to the audit records that are processed according to the feature definitions to generate intrusion detection rules. Results from the 1998 DARPA Intrusion Detection Evaluation showed that our ... (Update)

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BibTeX entry:   (Update)

Lee, W. and Stolfo, S. J., "A Framework for Constructing Features and Models for Intrusion Detection Systems," ACM Transactions on Information and System Security, vol. 3, November, 2000 http://citeseer.ist.psu.edu/article/lee00framework.html   More

@article{ lee00framework,
    author = "Wenke Lee and Salvatore J. Stolfo",
    title = "A framework for constructing features and models for intrusion detection systems",
    journal = "Information and System Security",
    volume = "3",
    number = "4",
    pages = "227--261",
    year = "2000",
    url = "citeseer.ist.psu.edu/article/lee00framework.html" }
Citations (may not include all citations):
976   Machine Learning (context) - Mitchell - 1997  ACM   DBLP
921   Mining association rules between sets of items in large data.. - Agrawal, Imielinski et al. - 1993  ACM   DBLP
248   Fast effective rule induction - Cohen - 1995
189   Discovering frequent episodes in sequences (context) - Mannila, Toivonen et al. - 1995  DBLP
142   A sense of self for Unix processes - Forrest, Hofmeyr et al. - 1996  ACM
132   EMERALD: Event monitoring enabling responses to anomalous li.. - Porras, Neumann - 1997
123   Bro: A system for detecting network intruders in real-time - Paxson - 1998  DBLP
121   Network intrusion detection (context) - Mukherjee, Heberlein et al. - 1994  ACM
106   The KDD process of extracting useful knowledge from volumes .. - Fayyad, Piatetsky-Shapiro et al. - 1996
105   State transition analysis: A rulebased intrusion detection a.. - Ilgun, Kemmerer et al. - 1995
86   JAM: Java agents for meta-learning over distributed database.. - Stolfo, Prodromidis et al. - 1997  DBLP
85   Discovering generalized episodes using minimal occurrences - Mannila, Toivonen - 1996  DBLP
84   Data mining approaches for intrusion detection - Lee, Stolfo - 1998
63   Next-generation intrusion detection expert system - Anderson, Frivold et al. - 1995
59   Toward parallel and distributed learning by metalearning - Chan, Stolfo - 1993
59   USTAT: A real-time intrusion detection system for Unix - Ilgun - 1992
58   available via anonymous ftp to ftp (context) - Jacobson, Leres et al. - 1989
56   A real-time intrusion detection expert system (context) - Lunt, Tamaru et al. - 1992
52   Automated detection of vulnerabilities in privileged program.. - Ko, Fink et al. - 1994
40   Temporal sequence learning and data reduction for anomaly de.. - Lane, Brodley - 1999
35   State of the practice of intrusion detection technologies - Allen, Christie et al. - 2000
32   A software architecture to support misuse intrusion detectio.. - Kumar, Spafford - 1995
27   A study in using neural networks for anomaly and misuse dete.. (context) - Ghosh, Schwartzbard - 1999
23   Detecting intruders in computer systems - Lunt - 1993
19   Mining in a data-flow environment: Experience in network int.. - Lee, Stolfo et al. - 1999  DBLP
17   A Data Mining Framework for Constructing Features and Models.. - Lee - 1999
15   Network flight recorder (context) - Recorder - 1997
1   A data mining framework for building intrusion detection mod.. (context) - for, Features et al. - 1999  DBLP



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