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JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
Avg review time: 58 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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Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions, LEOPOLDINO, A. L. M., FREITAS, C. M., MONTEIRO, L. F. C.
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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.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

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  4/2014 - 7

 HIGHLY CITED PAPER 

Intrusion Detection in NEAR System by Anti-denoising Traffic Data Series using Discrete Wavelet Transform

VANCEA, F. See more information about VANCEA, F. on SCOPUS See more information about VANCEA, F. on IEEExplore See more information about VANCEA, F. on Web of Science
 
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Download PDF pdficon (1,213 KB) | Citation | Downloads: 663 | Views: 1,025

Author keywords
discrete wavelet transform, intrusion detection, self-similarity, signal denoising, time-frequency analysis

References keywords
traffic(9), wavelet(8), processing(7), network(7), detection(7), signal(6), analysis(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-11-30
Volume 14, Issue 4, Year 2014, On page(s): 43 - 48
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.04007
Web of Science Accession Number: 000348772500007
SCOPUS ID: 84921631227

Abstract
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The paper presents two methods for detecting anomalies in data series derived from network traffic. Intrusion detection systems based on network traffic analysis are able to respond to incidents never seen before by detecting anomalies in data series extracted from the traffic. Some anomalies manifest themselves as pulses of various sizes and shapes, superimposed on series corresponding to normal traffic. In order to detect those impulses we propose two methods based on discrete wavelet transformation. Their effectiveness expressed in relative thresholds on pulse amplitude for no false negatives and no false positives is then evaluated against pulse duration and Hurst characteristic of original series. Different base functions are also evaluated for efficiency in the context of the proposed methods.


References | Cited By

Cited-By Clarivate Web of Science

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SCOPUS® Times Cited: 4
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Cited-By CrossRef

[1] Performance of discrete wavelet transform‐based method in the detection of influenza outbreaks in Iran: An ecological study, Minaeian, Sara, Alimohamadi, Yousef, Eshrati, Babak, Esmaeilzadeh, Firooz, Health Science Reports, ISSN 2398-8835, Issue 5, Volume 6, 2023.
Digital Object Identifier: 10.1002/hsr2.1245
[CrossRef]

[2] A new approach for internet traffic classification: GA-WK-ELM, Ertam, Fatih, Avcı, Engin, Measurement, ISSN 0263-2241, Issue , 2017.
Digital Object Identifier: 10.1016/j.measurement.2016.10.001
[CrossRef]

[3] RULE GENERATION BASED ON MODIFIED CUTTLEFISH ALGORITHM FOR INTRUSION DETECTION SYSTEM, EESA, Adel Sabry, SADIQ, Sheren, HASSAN, Masoud, ORMAN, Zeynep, Uludağ University Journal of The Faculty of Engineering, ISSN 2148-4147, 2021.
Digital Object Identifier: 10.17482/uumfd.747078
[CrossRef]

[4] Some results on intrusion and anomaly detection using signal processing and NEAR system, Vancea, Florin, Vancea, Codruta, 2015 38th International Conference on Telecommunications and Signal Processing (TSP), ISBN 978-1-4799-8498-5, 2015.
Digital Object Identifier: 10.1109/TSP.2015.7296234
[CrossRef]

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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