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JCR Impact Factor: 0.700
JCR 5-Year IF: 0.700
SCOPUS CiteScore: 1.8
Issues per year: 4
Current issue: Nov 2024
Next issue: Feb 2025
Avg review time: 56 days
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PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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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: 1,506 | Views: 2,565

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.


References | Cited By

Cited-By Clarivate Web of Science

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Cited-By SCOPUS

SCOPUS® Times Cited: 6
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Cited-By CrossRef

[1] Biometric Identification Advances: Unimodal to Multimodal Fusion of Face, Palm, and Iris Features, KADHIM, O. N., ABDULAMEER, M. H., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 1, Volume 24, 2024.
Digital Object Identifier: 10.4316/AECE.2024.01010
[CrossRef] [Full text]

[2] A Novel Approach to Speech Enhancement Based on Deep Neural Networks, SALEHI, M., MIRZAKUCHAKI, S., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 2, Volume 22, 2022.
Digital Object Identifier: 10.4316/AECE.2022.02009
[CrossRef] [Full text]

[3] Security-ANODR for provisioning of neighbour security in MANETs, Uppe, Nanaji, Mohan, Rao C. P. V. N. J., i-manager’s Journal on Wireless Communication Networks, ISSN 2319-4839, Issue 2, Volume 12, 2024.
Digital Object Identifier: 10.26634/jwcn.12.2.20868
[CrossRef]

[4] 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]

[5] Yapay zeka tarafından kontrol edilen yeni bir termoelektrik CPU soğutma sistemi, UMUT, İlhan, AKAL, Dinçer, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, ISSN 1300-1884, Issue 1, Volume 39, 2023.
Digital Object Identifier: 10.17341/gazimmfd.1150632
[CrossRef]

[6] Renyi entropy-driven network traffic anomaly detection with dynamic threshold, Yu, Haoran, Yang, Wenchuan, Cui, Baojiang, Sui, Runqi, Wu, Xuedong, Cybersecurity, ISSN 2523-3246, Issue 1, Volume 7, 2024.
Digital Object Identifier: 10.1186/s42400-024-00249-1
[CrossRef]

[7] Classification of Network Traffic and Anomaly Detection Using Entropy in NetFlow Records, Fosić, Igor, Žagar, Drago, 2024 International Symposium ELMAR, ISBN 979-8-3503-7542-8, 2024.
Digital Object Identifier: 10.1109/ELMAR62909.2024.10694414
[CrossRef]

[8] 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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