research-article

Using extreme learning machine for intrusion detection in a big data environment

Authors:

Magnus Westerlund,

Göran PulkkisAuthors Info & Claims

AISec '14: Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop

Pages 73 - 82

https://doi.org/10.1145/2666652.2666664

Published: 07 November 2014 Publication History

Abstract

Extending state-of-the-art machine learning algorithms to highly scalable (big data) analysis environments is crucial for the handling of authentic datasets in Intrusion Detection Systems (IDS). Traditional supervised learning methods are considered to be too slow for use in these environments. Therefore, we propose the use of Extreme Learning Machine (ELM) for detecting network intrusion attempts. We show they hold great promise for the field by employing a MapReduce based variant evaluated on the open source tool Hadoop.

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Cited By

Gelvez-Almeida EMora MBarrientos RHernández-García RVilches-Ponce KVera M(2024)A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural NetworksMathematical and Computational Applications10.3390/mca2903004029:3(40)Online publication date: 27-May-2024
https://doi.org/10.3390/mca29030040
Ponmalar ADhanakoti V(2022)Hybrid Whale Tabu algorithm optimized convolutional neural network architecture for intrusion detection in big dataConcurrency and Computation: Practice and Experience10.1002/cpe.703834:19Online publication date: 18-May-2022
https://doi.org/10.1002/cpe.7038
Yang WWang SJohnstone M(2021)A Comparative Study of ML-ELM and DNN for Intrusion DetectionProceedings of the 2021 Australasian Computer Science Week Multiconference10.1145/3437378.3437390(1-7)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1145/3437378.3437390
Show More Cited By

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Using extreme learning machine for intrusion detection in a big data environment

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Published In

cover image ACM Conferences

AISec '14: Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop

November 2014

134 pages

ISBN:9781450331531

DOI:10.1145/2666652

General Chair:
Gail-Joon Ahn
Arizona State University, USA
,
Program Chairs:
Christos Dimitrakakis
Chalmers University of Technology, Sweden
,
Aikaterini Mitrokotsa
Chalmers University of Technology, Sweden
,
Benjamin I.P. Rubinstein
University of Melbourne, Australia

Copyright © 2014 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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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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Association for Computing Machinery

New York, NY, United States

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Published: 07 November 2014

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CCS'14

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CCS'14: 2014 ACM SIGSAC Conference on Computer and Communications Security

November 7, 2014

Arizona, Scottsdale, USA

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AISec '14 Paper Acceptance Rate 12 of 24 submissions, 50%;

Overall Acceptance Rate 94 of 231 submissions, 41%

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Cited By

Gelvez-Almeida EMora MBarrientos RHernández-García RVilches-Ponce KVera M(2024)A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural NetworksMathematical and Computational Applications10.3390/mca2903004029:3(40)Online publication date: 27-May-2024
https://doi.org/10.3390/mca29030040
Ponmalar ADhanakoti V(2022)Hybrid Whale Tabu algorithm optimized convolutional neural network architecture for intrusion detection in big dataConcurrency and Computation: Practice and Experience10.1002/cpe.703834:19Online publication date: 18-May-2022
https://doi.org/10.1002/cpe.7038
Yang WWang SJohnstone M(2021)A Comparative Study of ML-ELM and DNN for Intrusion DetectionProceedings of the 2021 Australasian Computer Science Week Multiconference10.1145/3437378.3437390(1-7)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1145/3437378.3437390
Saravanan SPrakash G(2021)A Comprehensive Survey on Big Data Technology Based Cybersecurity Analytics SystemsApplied Soft Computing and Communication Networks10.1007/978-981-33-6173-7_9(123-143)Online publication date: 2-Jul-2021
https://doi.org/10.1007/978-981-33-6173-7_9
Li JWu WXue D(2020)An intrusion detection method based on active transfer learningIntelligent Data Analysis10.3233/IDA-19448724:2(363-383)Online publication date: 27-Mar-2020
https://doi.org/10.3233/IDA-194487
Hindy HBrosset DBayne ESeeam ATachtatzis CAtkinson RBellekens X(2020)A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection SystemsIEEE Access10.1109/ACCESS.2020.30001798(104650-104675)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3000179
Sovilj DBudnarain PSanner SSalmon GRao M(2020)A Comparative Evaluation of Unsupervised Deep Architectures for Intrusion Detection in Sequential Data StreamsExpert Systems with Applications10.1016/j.eswa.2020.113577(113577)Online publication date: May-2020
https://doi.org/10.1016/j.eswa.2020.113577
HAMZA OMOHAMMED O(2019)ON THE USE OF EXTREME LEARNING MACHINES FOR DETECTING ANOMALIES IN STUDENTS' RESULTSi-manager's Journal on Computer Science10.26634/jcom.6.4.157246:4(34)Online publication date: 2019
https://doi.org/10.26634/jcom.6.4.15724
Sharma JGiri CGranmo OGoodwin M(2019)Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregationEURASIP Journal on Information Security10.1186/s13635-019-0098-y2019:1Online publication date: 22-Oct-2019
https://doi.org/10.1186/s13635-019-0098-y
Ullah FBabar M(2019)An Architecture-Driven Adaptation Approach for Big Data Cyber Security Analytics2019 IEEE International Conference on Software Architecture (ICSA)10.1109/ICSA.2019.00013(41-50)Online publication date: Mar-2019
https://doi.org/10.1109/ICSA.2019.00013
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