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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  4/2021 - 6

 HIGHLY CITED PAPER 

Machine Learning Enhanced Entropy-Based Network Anomaly Detection

TIMCENKO, V. See more information about TIMCENKO, V. on SCOPUS See more information about TIMCENKO, V. on IEEExplore See more information about TIMCENKO, V. on Web of Science, GAJIN, S. See more information about GAJIN, S. on SCOPUS See more information about GAJIN, S. on SCOPUS See more information about GAJIN, S. on Web of Science
 
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Download PDF pdficon (1,765 KB) | Citation | Downloads: 1,303 | Views: 1,365

Author keywords
clustering algorithms, data flow computing, entropy, intrusion detection, machine learning

References keywords
detection(22), network(21), security(10), intrusion(10), data(10), anomaly(10), systems(9), learning(8), entropy(8), machine(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2021-11-30
Volume 21, Issue 4, Year 2021, On page(s): 51 - 60
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.04006
Web of Science Accession Number: 000725107100006
SCOPUS ID: 85122239638

Abstract
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The advanced development of new technologies and heterogeneous environments relies on the proper processing of large data volumes, and accurate and fast response of real-time applications. Such circumstances provide a fertile ground for the appearance of diverse security concerns, thus challenging the scientific community for building more reliable and efficient Network Anomaly Detection Systems. This research proposes a comprehensive flow-based anomaly detection architecture, which encompasses techniques for entropy-based data processing and machine learning-based attack detection. It encompasses several attack categories and relies on the use of modelled and synthetically generated traffic patterns for Port Scan, Network Scan, DDoS amplification, flood, and dictionary attacks. The entropy-based analysis is used for easier detection of the hidden traffic patterns, as it can capture the behaviour of the biggest contributors, and of a large number of minor appearances in the feature distribution. The unusual traffic is then processed by the use of unsupervised machine learning algorithms. The approach is verified with datasets based on real network traffic, synthetically generated attack traffic instances and botnet traffic. The architecture is an original solution, planned for further real-network application, targeting the possible support for a range of different use cases.


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References Weight

Web of Science® Citations for all references: 8,162 TCR
SCOPUS® Citations for all references: 49,455 TCR

Web of Science® Average Citations per reference: 163 ACR
SCOPUS® Average Citations per reference: 989 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-05-14 20:07 in 380 seconds.




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