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Abstract: In this paper we describe our preliminary experiments to extend the work pioneered by Forrest (see Forrest et al. 1996) on learning the (normal and abnormal) patterns of Unix processes. These patterns can be used to identify misuses of and intrusions in Unix systems. We formulated machine learning tasks on operating system call sequences of normal and abnormal (intrusion) executions of the Unix sendmail program. We show that our methods can accurately distinguish all abnormal executions of... (Update)
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BibTeX entry: (Update)
W. Lee, S. J. Stolfo, and P. K. Chan. Learning patterns from unix process execution traces for intrusion detection. In AAAI Workshop: AI Approaches to Fraud Detection and Risk Management, pages 50--56. AAAI Press, July 1997. http://citeseer.ist.psu.edu/lee97learning.html More
@inproceedings{ lee97learning,
author = "Wenke Lee and Salvatore J. Stolfo and Philip K. Chan",
title = "Learning Patterns from Unix Process Execution Traces for Intrusion Detection",
booktitle = "Proceedings of the AAAI97 workshop on AI Approaches to Fraud Detection and Risk Management",
pages = "50--56",
location = "Providence, RI",
year = "1997",
publisher = "AAAI Press",
url = "citeseer.ist.psu.edu/lee97learning.html" }
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59 Toward Parallel and Distributed Learning by Meta-Learning - Chan, Stolfo - 1993
56 A Real-time Intrusion Detection Expert System (context) - Lunt, Tamaru et al. - 1992
32 A Software Architecture to Support Misuse Intrusion Detectio.. - Kumar, Spafford - 1995
29 Credit Card Fraud Detection Using Meta-Learning: Issues and .. - Stolfo, Fan et al. - 1997
24 Searching for Structure in Multiple Streams of Data - Oates, Cohen - 1996
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