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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

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


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ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection, SARACOGLU, O. G., BAGIS, A., KONAR, M., TABARU, T. E.
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Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

We have the confirmation Advances in Electrical and Computer Engineering will be included in the Gale database.

IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  4/2014 - 7

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: 258 | Views: 1,217

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

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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  «-- Click to see who has cited this paper

[1] M. Roesch, "Snort - Lightweight Intrusion Detection for Networks", Proceedings of LISA '99, p.229-238

[2] P. Barford, J. Kline, D. Plonka and A. Ron, "A Signal Analysis of Network Traffic Anomalies", Proceedings Of ACM Sigcomm Internet Measurement Workshop 2002,

[3] H. Cheng, Y. Fang, J. Huang, Z. Shao, "Multifractal Analysis of Abnormal Network Traffic", APAN Network Research Workshop, 2004

[4] W. Lu, A. A. Ghorbani, "Network Anomaly Detection Based on Wavelet Analysis", EURASIP Journal on Advances in Signal Processing, Volume 2009, Article ID 837601, 16 pages,
[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 83]

[5] C. Huang, S. Thareja, Y. Shin, "Wavelet-based real time detection of network traffic anomalies," in Proc. Securecomm and Workshops, 2006, 1-4244-0423-1,
[CrossRef] [SCOPUS Times Cited 15]

[6] F. Vancea, C. Vancea, "NEAR - Network Extractor of Anomaly Records or Traffic Split-Counting for Anomaly Detection", Conference Proceedings EUROCON 2013

[7] W. Leland, M. Taqqu, W. Willinger, and D. Wilson, "On the self-similar nature of Ethernet traffic (extended version)," IEEE/ACM Trans. Networking, pp. 1-15, 1994
[CrossRef] [Web of Science Times Cited 2442] [SCOPUS Times Cited 3435]

[8] S. Uhlig and O. Bonaventure, "Understanding the Long-Term Self-Similarity of Internet Traffic", Proceedings of QOFIS2001, Coimbra, Portugal, September 2001. Springer-Verlag LNCS2156, pages 286-298

[9] J. M. Peha, "Protocols Can Make Traffic Appear Self-Similar", Department of Engineering and Public Policy. Paper 47.

[10] R. G. Clegg, "A Practical Guide To Measuring The Hurst Parameter", International Journal of Simulation: Systems, Science and Technology, ISSN 1473-804x online, 1473- 8031 print

[11] S. Stoev, M. S. Taqqu, C. Park, J. S. Marron, "On the wavelet spectrum diagnostic for Hurst parameter estimation in the analysis of Internet traffic", Computer Networks, vol.48, 2005, pp. 423-445
[CrossRef] [Web of Science Times Cited 66] [SCOPUS Times Cited 75]

[12] S. Katsev, I. L'Heureux, "Are Hurst exponents estimated from short or irregular time series meaningfult", Computers & Geosciences 29, Elsevier 2003, 1085-1089

[13] R. G. Clegg, R. Landa, M. Rio, "Criticisms of modelling packet traffic using long-range dependence (extended version)", Journal of Computer and System Sciences Volume 77, Issue 5, September 2011, Pages 861-868
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 3]

[14] F. Vancea, "On Performance of Simple Detection of Pulse-Shaped Anomalies in Data Series from NEAR Network Data Collection Tool", Buletinul Stiintific al Universitatii "Politehnica" din Timisoara, Tom 57(71), Fascicola 1-2, 2012

[15] S. Mallat, and W. L. Hwang. "Singularity detection and processing with wavelets." Information Theory, IEEE Transactions on 38, no. 2 (1992): 617-643.
[CrossRef] [Web of Science Times Cited 2253] [SCOPUS Times Cited 3014]

[16] O. Alyt AM, O. S. Abbas, and A. Z. Elsherbeni. "Detection and localization of RF radar pulses in noise environments using wavelet packet transform and higher order statistics." Progress In Electromagnetics Research 58 (2006): 301-317.

[17] Yi, Huiyue. "Robust Wavelet Transform-based Correlation Edge Detectors Using Correlation of Wavelet Coefficients.", International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 4, No. 4, December, 2011.

[18] J. F. Kaiser, "On Teager's algorithm and its generalization to continuous signals," Proc. 4th IEEE Digital Signal Processing Workshop, Mohonk (New Paltz), NY, Sept. 1990.

[19] A. Isar, I. Nafornita, "Time-frequency representations" "Reprezentari timp-frecventa", Ed. "Politehnica", 1998, p. 382-388

[20] M. Lang, H. Guo, J. E. Odegard, C. S. Burrus, and R. O. Wells Jr. "Noise reduction using an undecimated discrete wavelet transform." Signal Processing Letters, IEEE 3, no. 1 (1996): 10-12.
[CrossRef] [Web of Science Times Cited 221] [SCOPUS Times Cited 290]

[21] R. Coifman, Y. Meyer, S. Quake, and M. V. Wickerhauser. "Signal processing and compression with wavelet packets." In Wavelets and their applications, pp. 363-379. Springer Netherlands, 1994.

References Weight

Web of Science® Citations for all references: 5,015 TCR
SCOPUS® Citations for all references: 6,915 TCR

Web of Science® Average Citations per reference: 228 ACR
SCOPUS® Average Citations per reference: 314 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 2017-10-20 01:29 in 69 seconds.

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Faculty of Electrical Engineering and Computer Science
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