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Machine Learning Enhanced Entropy-Based Network Anomaly DetectionTIMCENKO, V. , GAJIN, S.
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clustering algorithms, data flow computing, entropy, intrusion detection, machine learning
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
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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Digital Object Identifier: 10.4316/AECE.2022.02009 [CrossRef] [Full text]
 A novel method for local anomaly detection of time series based on multi entropy fusion, Wang, Gangjin, Wei, Daijun, Li, Xiangbo, Wang, Ningkui, Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, Issue , 2023.
Digital Object Identifier: 10.1016/j.physa.2023.128593 [CrossRef]
 Yapay zeka tarafından kontrol edilen yeni bir termoelektrik CPU soğutma sistemi, UMUT, İlhan, AKAL, Dinçer, Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, ISSN 1300-1884, 2023.
Digital Object Identifier: 10.17341/gazimmfd.1150632 [CrossRef]
 Hybrid Machine Learning Traffic Flows Analysis for Network Attacks Detection, Timcenko, Valentina, Gajin, Slavko, 2022 30th Telecommunications Forum (TELFOR), ISBN 978-1-6654-7273-9, 2022.
Digital Object Identifier: 10.1109/TELFOR56187.2022.9983780 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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