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Parallel and Distributed Computing for Cybersecurity

Author:

Vipin KumarAuthors Info & Claims

IEEE Distributed Systems Online, Volume 6, Issue 10

Page 1

https://doi.org/10.1109/MDSO.2005.53

Published: 01 October 2005 Publication History

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Abstract

Parallel and distributed data mining offer great promise for addressing cybersecurity. The Minnesota Intrusion Detection System can detect sophisticated cyberattacks on large-scale networks that are hard to detect using signature-based systems.

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Wang YChen CLai JFu LZhou YZheng Z(2023)A Self-Representation Method with Local Similarity Preserving for Fast Multi-View Outlier DetectionACM Transactions on Knowledge Discovery from Data10.1145/353219117:1(1-20)Online publication date: 15-Mar-2023
https://dl.acm.org/doi/10.1145/3532191
Qi JBurnside GCoenen F(2021)Ranking Pathology Data in the Absence of a Ground TruthArtificial Intelligence XXXVIII10.1007/978-3-030-91100-3_18(209-223)Online publication date: 14-Dec-2021
https://dl.acm.org/doi/10.1007/978-3-030-91100-3_18
Carvalho JCarvalho SRocha Á(2020)European strategy and legislation for cybersecurity: implications for PortugalCluster Computing10.1007/s10586-020-03052-y23:3(1845-1854)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s10586-020-03052-y
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Parallel and Distributed Computing for Cybersecurity

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cover image IEEE Distributed Systems Online

IEEE Distributed Systems Online Volume 6, Issue 10

October 2005

ISSN:1541-4922

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IEEE Educational Activities Department

United States

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Published: 01 October 2005

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Wang YChen CLai JFu LZhou YZheng Z(2023)A Self-Representation Method with Local Similarity Preserving for Fast Multi-View Outlier DetectionACM Transactions on Knowledge Discovery from Data10.1145/353219117:1(1-20)Online publication date: 15-Mar-2023
https://dl.acm.org/doi/10.1145/3532191
Qi JBurnside GCoenen F(2021)Ranking Pathology Data in the Absence of a Ground TruthArtificial Intelligence XXXVIII10.1007/978-3-030-91100-3_18(209-223)Online publication date: 14-Dec-2021
https://dl.acm.org/doi/10.1007/978-3-030-91100-3_18
Carvalho JCarvalho SRocha Á(2020)European strategy and legislation for cybersecurity: implications for PortugalCluster Computing10.1007/s10586-020-03052-y23:3(1845-1854)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s10586-020-03052-y
Nyre-Yu MSprehn KCaldwell B(2019)Informing Hybrid System Design in Cyber Security Incident ResponseHCI for Cybersecurity, Privacy and Trust10.1007/978-3-030-22351-9_22(325-338)Online publication date: 26-Jul-2019
https://dl.acm.org/doi/10.1007/978-3-030-22351-9_22
Tgavalekos KNamayanja JAlhassan RBein D(2018)Characterization of network behavior to detect changesProceedings of the Workshop Program of the 19th International Conference on Distributed Computing and Networking10.1145/3170521.3170523(1-6)Online publication date: 4-Jan-2018
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Feng QZhang YLi CDou ZWang J(2017)Anomaly detection of spectrum in wireless communication via deep auto-encodersThe Journal of Supercomputing10.1007/s11227-017-2017-773:7(3161-3178)Online publication date: 1-Jul-2017
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Schneider MErtel WPalm G(2016)Constant time expected similarity estimation for large-scale anomaly detectionProceedings of the Twenty-second European Conference on Artificial Intelligence10.3233/978-1-61499-672-9-12(12-20)Online publication date: 29-Aug-2016
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Gupta AKohli S(2016)An MCDM approach towards handling outliers in web dataArtificial Intelligence Review10.1007/s10462-015-9456-446:1(59-82)Online publication date: 1-Jun-2016
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Shaikh SKitagawa H(2014)Top-k Outlier Detection from Uncertain DataInternational Journal of Automation and Computing10.1007/s11633-014-0775-811:2(128-142)Online publication date: 1-Apr-2014
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Chandola VBanerjee AKumar V(2009)Anomaly detectionACM Computing Surveys10.1145/1541880.154188241:3(1-58)Online publication date: 30-Jul-2009
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