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An introduction to intrusion detection

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Aurobindo SundaramAuthors Info & Claims

XRDS: Crossroads, The ACM Magazine for Students, Volume 2, Issue 4

Pages 3 - 7

https://doi.org/10.1145/332159.332161

Published: 01 April 1996 Publication History

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Ganeshan RSravani MKalidindi AOm Prakash P(2024)ShNFN: Shepard Neuro-Fuzzy Network for Intrusion Detection in Fog ComputingInternational Journal of Pattern Recognition and Artificial Intelligence10.1142/S021800142450018638:13Online publication date: 16-Oct-2024
https://doi.org/10.1142/S0218001424500186
Sithungu SEhlers E(2024)From Concept to Prototype: Developing and Testing GAAINet for Industrial IoT Intrusion DetectionIntelligent Information Processing XII10.1007/978-3-031-57808-3_33(453-468)Online publication date: 6-Apr-2024
https://doi.org/10.1007/978-3-031-57808-3_33
Odarchenko RIavich MIashvili GFedushko SSyerov Y(2023)Assessment of Security KPIs for 5G Network Slices for Special Groups of SubscribersBig Data and Cognitive Computing10.3390/bdcc70401697:4(169)Online publication date: 26-Oct-2023
https://doi.org/10.3390/bdcc7040169
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An introduction to intrusion detection

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Intrusion detection is a new, retrofit approach for providing a sense of security in existing computers and data networks, while allowing them to operate in their current "open" mode. The goal of intrusion detection is to identify unauthorized use, ...
Network intrusion detection: stepping-stone and masquerader detections
A hybrid intrusion detection system

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cover image XRDS: Crossroads, The ACM Magazine for Students

XRDS: Crossroads, The ACM Magazine for Students Volume 2, Issue 4

Special issue on computer security

March 1996

24 pages

ISSN:1528-4972

EISSN:1528-4980

DOI:10.1145/332159

Editor:
Lorrie Faith Cranor

Issue’s Table of Contents

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: 01 April 1996

Published in XRDS Volume 2, Issue 4

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Ganeshan RSravani MKalidindi AOm Prakash P(2024)ShNFN: Shepard Neuro-Fuzzy Network for Intrusion Detection in Fog ComputingInternational Journal of Pattern Recognition and Artificial Intelligence10.1142/S021800142450018638:13Online publication date: 16-Oct-2024
https://doi.org/10.1142/S0218001424500186
Sithungu SEhlers E(2024)From Concept to Prototype: Developing and Testing GAAINet for Industrial IoT Intrusion DetectionIntelligent Information Processing XII10.1007/978-3-031-57808-3_33(453-468)Online publication date: 6-Apr-2024
https://doi.org/10.1007/978-3-031-57808-3_33
Odarchenko RIavich MIashvili GFedushko SSyerov Y(2023)Assessment of Security KPIs for 5G Network Slices for Special Groups of SubscribersBig Data and Cognitive Computing10.3390/bdcc70401697:4(169)Online publication date: 26-Oct-2023
https://doi.org/10.3390/bdcc7040169
İLGÜN ESAMET R(2023)Veri setine uygulanan ön işlemler ile makine öğrenimi yöntemi kullanılarak geliştirilen saldırı tespit modellerinin performanslarının arttırılmasıGazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi10.17341/gazimmfd.112202139:2(679-692)Online publication date: 30-Nov-2023
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Unger SRaak COstermann T(2023)Reliability and Performance of the Online Literature Database CAMbase after Changing from a Semantic Search to a Score Ranking AlgorithmSN Computer Science10.1007/s42979-023-02146-94:5Online publication date: 9-Sep-2023
https://dl.acm.org/doi/10.1007/s42979-023-02146-9
ZHANG FZHEN PJING DTANG XCHEN HYAN J(2022)SVM Based Intrusion Detection Method with Nonlinear Scaling and Feature SelectionIEICE Transactions on Information and Systems10.1587/transinf.2021EDP7184E105.D:5(1024-1038)Online publication date: 1-May-2022
https://doi.org/10.1587/transinf.2021EDP7184
Xie JLi SZhang YSun PXu H(2022)Analysis and Detection against Network Attacks in the Overlapping Phenomenon of Behavior AttributeComputers & Security10.1016/j.cose.2022.102867121(102867)Online publication date: Oct-2022
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Utimura LCosta KScherer R(2022)Real-time application of OPF-based classifier in Snort IDSOptimum-Path Forest10.1016/B978-0-12-822688-9.00011-6(55-93)Online publication date: 2022
https://doi.org/10.1016/B978-0-12-822688-9.00011-6
Cheikh MHacini SBoufaida Z(2021)Visualization Technique for Intrusion DetectionResearch Anthology on Combating Denial-of-Service Attacks10.4018/978-1-7998-5348-0.ch009(168-182)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-5348-0.ch009
Perumalla SChatterjee SKumar A(2021)Block Chain-based access control and intrusion detection system in IoD2021 6th International Conference on Communication and Electronics Systems (ICCES)10.1109/ICCES51350.2021.9488948(511-518)Online publication date: 8-Jul-2021
https://doi.org/10.1109/ICCES51350.2021.9488948
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References

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Recommendations

Network intrusion detection

Network intrusion detection: stepping-stone and masquerader detections

A hybrid intrusion detection system

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