Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.

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Published by
The International Institute of Informatics and Cybernetics


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Academia.edu
(A Community of about 40.000.000 Academics)


Honorary Editorial Advisory Board's Chair
William Lesso (1931-2015)

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org
 

Editorial Advisory Board

Quality Assurance

Editors

Journal's Reviewers
Call for Special Articles
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


Transfer Learning for Facial Emotion Recognition on Small Datasets
Paolo Barile, Clara Bassano, Paolo Piciocchi
(pages: 1-5)

How to Link Educational Purposes and Immersive Video Games Development? An Ontological Approach Proposal
Nathan Aky
(pages: 6-13)

Application of Building Information Modeling (BIM) in the Planning and Construction of a Building
Renata Maria Abrantes Baracho, Luiz Gustavo da Silva Santiago, Antonio Tagore Assumpção Mendoza e Silva, Marcelo Franco Porto
(pages: 14-19)

Transformative, Transdisciplinary, Transcendent Digital Education: Synergy, Sustainability and Calamity
Rusudan Makhachashvili, Ivan Semenist
(pages: 20-27)

New Online Tools for the Data Visualization of Bivalve Molluscs' Production Areas of Veneto Region
Eleonora Franzago, Claudia Casarotto, Matteo Trolese, Marica Toson, Mirko Ruzza, Manuela Dalla Pozza, Grazia Manca, Giuseppe Arcangeli, Nicola Ferrè, Laura Bille
(pages: 28-32)

Geodata Processing Methodology on GIS Platforms When Creating Spatial Development Plans of Territorial Communities: Case of Ukraine
Olena Kopishynska, Yurii Utkin, Ihor Sliusar, Leonid Flehantov, Mykola Somych, Oksana Yakovlieva, Olena Scryl
(pages: 33-40)

D-CIDE: An Interactive Code Learning Program
Lukas Grant, Matthew F. Tennyson, Jason Owen
(pages: 41-46)

Interdisciplinary Digital Skills Development for Educational Communication: Emergency and Ai-Enhanced Digitization
Rusudan Makhachashvili, Ivan Semenist, Ganna Prihodko, Irina Kolegaeva, Olexandra Prykhodchenko, Olena Tupakhina
(pages: 47-51)

Interdisciplinarity in Smart Systems Applied to Rural School Transport in Brazil
Renata Maria Abrantes Baracho, Mozart Joaquim Magalhães Vidigal, Marcelo Franco Porto, Beatriz Couto
(pages: 52-59)

Peculiarities of the Realization of IT Projects for the Implementation of ERP Systems on the Path of Digitalization of Territorial Communities Activities
Olena Kopishynska, Yurii Utkin, Ihor Sliusar, Khanlar Makhmudov, Olena Kalashnyk, Svitlana Moroz, Olena Kyrychenko
(pages: 60-67)


 

Abstracts

 


ABSTRACT


Machine Learning Based IP Network Traffic Classification Using Feature Significance Analysis

Te-Shun Chou, John Pickard, Ciprian Popoviciu


After over 30-year deployment, IPv4 addresses are running short on supply with the growth of the Internet so dynamic. A new technology will take its place, IPv6, an evolution from IPv4 that includes virtually unlimited address space. However, it will take time to totally transit from IPv4 to IPv6. IPv6 will coexist with IPv4 for a period of time and then eventually replace IPv4.

This paper studied network traffic that included information of both IPv4 and IPv6. The traffic was collected from 600 US government websites that were all reported to have Domain Name Services (DNS) and Web services accessible over IPv4 and IPv6. Cloud based, Internet distributed monitoring agents were deployed in eight geographic locations to collect data. Both feature selection algorithms, filter and wrapper, were applied to the dataset and the classification accuracy was then studied. The results showed that feature selection algorithms effectively reduced the complexity of the classification model. The results also confirmed that the reduced feature set contributed a superior classification performance over full feature set.

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