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Article

ADMIT: anomaly-based data mining for intrusions

Authors:

Karlton Sequeira,

Mohammed ZakiAuthors Info & Claims

KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 386 - 395

https://doi.org/10.1145/775047.775103

Published: 23 July 2002 Publication History

Abstract

Security of computer systems is essential to their acceptance and utility. Computer security analysts use intrusion detection systems to assist them in maintaining computer system security. This paper deals with the problem of differentiating between masqueraders and the true user of a computer terminal. Prior efficient solutions are less suited to real time application, often requiring all training data to be labeled, and do not inherently provide an intuitive idea of what the data model means. Our system, called ADMIT, relaxes these constraints, by creating user profiles using semi-incremental techniques. It is a real-time intrusion detection system with host-based data collection and processing. Our method also suggests ideas for dealing with concept drift and affords a detection rate as high as 80.3% and a false positive rate as low as 15.3%.

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Dani SThakur CNagvanshi NSingh G(2024)Anomaly Detection using PCA in Time Series Data2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)10.1109/IATMSI60426.2024.10502929(1-6)Online publication date: 14-Mar-2024
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Published In

cover image ACM Conferences

KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining

July 2002

719 pages

ISBN:158113567X

DOI:10.1145/775047

Conference Chair:
Osmar R. Zaïane
University of Alberta, Canada
,
General Chair:
Randy Goebel
University of Alberta, Canada
,
Program Chairs:
David Hand
Imperial College, UK
,
Daniel Keim
AT&T
,
Raymond Ng
University of British Columbia, Canada

Copyright © 2002 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 23 July 2002

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KDD02: The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

July 23 - 26, 2002

Alberta, Edmonton, Canada

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KDD '02 Paper Acceptance Rate 44 of 307 submissions, 14%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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https://doi.org/10.1109/ICSIP61881.2024.10671411
Dani SThakur CNagvanshi NSingh G(2024)Anomaly Detection using PCA in Time Series Data2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)10.1109/IATMSI60426.2024.10502929(1-6)Online publication date: 14-Mar-2024
https://doi.org/10.1109/IATMSI60426.2024.10502929
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https://dl.acm.org/doi/10.1016/j.cose.2024.103961
Liu SMa SChen HCui LDing J(2024)Combining KNN with AutoEncoder for Outlier DetectionJournal of Computer Science and Technology10.1007/s11390-023-2403-y39:5(1153-1166)Online publication date: 5-Dec-2024
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Cerdà-Alabern LIuhasz GGemmi G(2023)Anomaly detection for fault detection in wireless community networks using machine learningComputer Communications10.1016/j.comcom.2023.02.019202:C(191-203)Online publication date: 15-Mar-2023
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Potula SSelvanambi RKaruppiah MPelusi D(2023)Artificial Intelligence-Based Cyber Security ApplicationsArtificial Intelligence and Cyber Security in Industry 4.010.1007/978-981-99-2115-7_16(343-373)Online publication date: 14-Jun-2023
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