IEICE TRANSACTIONS on Information
A New Intrusion Detection Method Based on Discriminant Analysis
Summary :
Many methods have been proposed to detect intrusions; for example, the pattern matching method on known intrusion patterns and the statistical approach to detecting deviation from normal activities. We investigated a new method for detecting intrusions based on the number of system calls during a user's network activity on a host machine. This method attempts to separate intrusions from normal activities by using discriminant analysis, a kind of multivariate analysis. We can detect intrusions by analyzing only 11 system calls occurring on a host machine by discriminant analysis with the Mahalanobis' distance, and can also tell whether an unknown sample is an intrusion. Our approach is a lightweight intrusion detection method, given that it requires only 11 system calls for analysis. Moreover, our approach does not require user profiles or a user activity database in order to detect intrusions. This paper explains our new method for the separation of intrusions and normal behavior by discriminant analysis, and describes the classification method by which to identify an unknown behavior.
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- IEICE TRANSACTIONS on Information Vol.E84-D No.5 pp.570-577
- Publication Date
- 2001/05/01
- Publicized
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- Type of Manuscript
- Special Section PAPER (Special Issue on High-speed Internet Technology and its Applications)
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Midori ASAKA, Takefumi ONABUTA, Tadashi INOUE, Shunji OKAZAWA, Shigeki GOTO, "A New Intrusion Detection Method Based on Discriminant Analysis" in IEICE TRANSACTIONS on Information,
vol. E84-D, no. 5, pp. 570-577, May 2001, doi: .
Abstract: Many methods have been proposed to detect intrusions; for example, the pattern matching method on known intrusion patterns and the statistical approach to detecting deviation from normal activities. We investigated a new method for detecting intrusions based on the number of system calls during a user's network activity on a host machine. This method attempts to separate intrusions from normal activities by using discriminant analysis, a kind of multivariate analysis. We can detect intrusions by analyzing only 11 system calls occurring on a host machine by discriminant analysis with the Mahalanobis' distance, and can also tell whether an unknown sample is an intrusion. Our approach is a lightweight intrusion detection method, given that it requires only 11 system calls for analysis. Moreover, our approach does not require user profiles or a user activity database in order to detect intrusions. This paper explains our new method for the separation of intrusions and normal behavior by discriminant analysis, and describes the classification method by which to identify an unknown behavior.
URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_5_570/_p
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@ARTICLE{e84-d_5_570,
author={Midori ASAKA, Takefumi ONABUTA, Tadashi INOUE, Shunji OKAZAWA, Shigeki GOTO, },
journal={IEICE TRANSACTIONS on Information},
title={A New Intrusion Detection Method Based on Discriminant Analysis},
year={2001},
volume={E84-D},
number={5},
pages={570-577},
abstract={Many methods have been proposed to detect intrusions; for example, the pattern matching method on known intrusion patterns and the statistical approach to detecting deviation from normal activities. We investigated a new method for detecting intrusions based on the number of system calls during a user's network activity on a host machine. This method attempts to separate intrusions from normal activities by using discriminant analysis, a kind of multivariate analysis. We can detect intrusions by analyzing only 11 system calls occurring on a host machine by discriminant analysis with the Mahalanobis' distance, and can also tell whether an unknown sample is an intrusion. Our approach is a lightweight intrusion detection method, given that it requires only 11 system calls for analysis. Moreover, our approach does not require user profiles or a user activity database in order to detect intrusions. This paper explains our new method for the separation of intrusions and normal behavior by discriminant analysis, and describes the classification method by which to identify an unknown behavior.},
keywords={},
doi={},
ISSN={},
month={May},}
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TY - JOUR
TI - A New Intrusion Detection Method Based on Discriminant Analysis
T2 - IEICE TRANSACTIONS on Information
SP - 570
EP - 577
AU - Midori ASAKA
AU - Takefumi ONABUTA
AU - Tadashi INOUE
AU - Shunji OKAZAWA
AU - Shigeki GOTO
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E84-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2001
AB - Many methods have been proposed to detect intrusions; for example, the pattern matching method on known intrusion patterns and the statistical approach to detecting deviation from normal activities. We investigated a new method for detecting intrusions based on the number of system calls during a user's network activity on a host machine. This method attempts to separate intrusions from normal activities by using discriminant analysis, a kind of multivariate analysis. We can detect intrusions by analyzing only 11 system calls occurring on a host machine by discriminant analysis with the Mahalanobis' distance, and can also tell whether an unknown sample is an intrusion. Our approach is a lightweight intrusion detection method, given that it requires only 11 system calls for analysis. Moreover, our approach does not require user profiles or a user activity database in order to detect intrusions. This paper explains our new method for the separation of intrusions and normal behavior by discriminant analysis, and describes the classification method by which to identify an unknown behavior.
ER -
English
