An Application of Machine Learning to Anomaly Detection

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Author

T. Lane and C. Brodley

Entry type

article

Abstract

The anomaly detection problem has been widely studied in the computer security literature. In this paper we present a machine learning approach to anomaly detection. Our system builds user profiles based on command sequences and compares current input sequences to the profile using a similarity measure. The system must learn to classify current behavior as consistent or anomalous with past behavior using only positive examples of the account's valid user. Our empirical results demonstrate that this is a promising approach to distinguishing the legitamate user from an intruder

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Date

1997 – February – 14

URL

https://www.cerias.purdue.edu/ ... orts-ssl/public/97-03.pdf

Key alpha

Lane

Number

COAST TR 97-03

Affiliation

Purdue University

Publication Date

0000-00-00

Keywords

computer security, anomaly detection, machine learning

Language

English

Location

A hard-copy of this is in the CERIAS Library

Subject

Computer security

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@Article{ Lane,
	title = "An Application of Machine Learning to Anomaly Detection",
	author = "T. Lane and C. Brodley",
	year = "1997",
	month = "February",
	number = "COAST TR 97-03",
	day = "14",
	abstract = "The anomaly detection problem has been widely studied in the computer security literature.  In this paper we present a machine learning approach to anomaly detection.  Our system builds user profiles based on command sequences and compares current input sequences to the profile using a similarity measure.  The system must learn to classify current behavior as consistent or anomalous with past behavior using only positive examples of the account's valid user.  Our empirical results demonstrate that this is a promising approach to distinguishing the legitamate user from an intruder",
	affiliation = "Purdue University",
	keywords = "computer security, anomaly detection, machine learning",
	language = "English",
	subject = "Computer security",
	url = "https://www.cerias.purdue.edu/techreports-ssl/public/97-03.pdf",
}