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Visual analytics with decision tree on network traffic flow for botnet detection

Visual analytics (VA) is an integral approach combining visualization, human factors, and data analysis. VA can synthesize information and derive insight from massive, dynamic, ambiguous and often conflicting data. Thus, help discover the expected and unexpected information. Moreover, the visualizat...

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Main Authors: Muhammad, Muhammad Khairul Rijal, Mohd. Azmi, Nurulhuda Firdaus, Amir Sjarif, Nilam Nur, Ismail, Saiful Adli, Ya’acob, Suraya, Che Mohd. Yusof, Rasimah
Format: Article
Language:English
Published: International Center for Scientific Research and Studies 2018
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/86502/1/MuhammadKhairulRijal2018_VisualAnalyticswithDecisionTreeonNetwork.pdf
http://eprints.utm.my/id/eprint/86502/
http://home.ijasca.com/data/documents/4_Pg_72-91_Visual-Analytics-with-Decision-Tree-on-Network-Traffic-Flow-for-Botnet-Detection.pdf
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id my.utm.86502
record_format eprints
spelling my.utm.865022020-09-30T08:41:12Z http://eprints.utm.my/id/eprint/86502/ Visual analytics with decision tree on network traffic flow for botnet detection Muhammad, Muhammad Khairul Rijal Mohd. Azmi, Nurulhuda Firdaus Amir Sjarif, Nilam Nur Ismail, Saiful Adli Ya’acob, Suraya Che Mohd. Yusof, Rasimah T Technology (General) Visual analytics (VA) is an integral approach combining visualization, human factors, and data analysis. VA can synthesize information and derive insight from massive, dynamic, ambiguous and often conflicting data. Thus, help discover the expected and unexpected information. Moreover, the visualization could support the assessment in a timely period on which pre-emptive action can be taken. This paper discusses the implementation of visual analytics with decision tree model on network traffic flow for botnet detection. The discussion covers scenarios based on workstation, network traffic ranges and times. The experiment consists of data modeling, analytics and visualization using Microsoft PowerBI platform. Five different VA with different scenario for botnet detection is examined and analysis. From the studies, it may provide visual analytics as flexible approach for botnet detection on network traffic flow by being able to add more information related to botnet, increase path for data exploration and increase the effectiveness of analytics tool. Moreover, learning the pattern of communication and identified which is a normal behavior and abnormal behavior will be vital for security visual analyst as a future reference. International Center for Scientific Research and Studies 2018-11 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/86502/1/MuhammadKhairulRijal2018_VisualAnalyticswithDecisionTreeonNetwork.pdf Muhammad, Muhammad Khairul Rijal and Mohd. Azmi, Nurulhuda Firdaus and Amir Sjarif, Nilam Nur and Ismail, Saiful Adli and Ya’acob, Suraya and Che Mohd. Yusof, Rasimah (2018) Visual analytics with decision tree on network traffic flow for botnet detection. International Journal of Advances in Soft Computing and its Applications, 10 (3). pp. 73-91. ISSN 2074-2827 http://home.ijasca.com/data/documents/4_Pg_72-91_Visual-Analytics-with-Decision-Tree-on-Network-Traffic-Flow-for-Botnet-Detection.pdf
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Muhammad, Muhammad Khairul Rijal
Mohd. Azmi, Nurulhuda Firdaus
Amir Sjarif, Nilam Nur
Ismail, Saiful Adli
Ya’acob, Suraya
Che Mohd. Yusof, Rasimah
Visual analytics with decision tree on network traffic flow for botnet detection
description Visual analytics (VA) is an integral approach combining visualization, human factors, and data analysis. VA can synthesize information and derive insight from massive, dynamic, ambiguous and often conflicting data. Thus, help discover the expected and unexpected information. Moreover, the visualization could support the assessment in a timely period on which pre-emptive action can be taken. This paper discusses the implementation of visual analytics with decision tree model on network traffic flow for botnet detection. The discussion covers scenarios based on workstation, network traffic ranges and times. The experiment consists of data modeling, analytics and visualization using Microsoft PowerBI platform. Five different VA with different scenario for botnet detection is examined and analysis. From the studies, it may provide visual analytics as flexible approach for botnet detection on network traffic flow by being able to add more information related to botnet, increase path for data exploration and increase the effectiveness of analytics tool. Moreover, learning the pattern of communication and identified which is a normal behavior and abnormal behavior will be vital for security visual analyst as a future reference.
format Article
author Muhammad, Muhammad Khairul Rijal
Mohd. Azmi, Nurulhuda Firdaus
Amir Sjarif, Nilam Nur
Ismail, Saiful Adli
Ya’acob, Suraya
Che Mohd. Yusof, Rasimah
author_facet Muhammad, Muhammad Khairul Rijal
Mohd. Azmi, Nurulhuda Firdaus
Amir Sjarif, Nilam Nur
Ismail, Saiful Adli
Ya’acob, Suraya
Che Mohd. Yusof, Rasimah
author_sort Muhammad, Muhammad Khairul Rijal
title Visual analytics with decision tree on network traffic flow for botnet detection
title_short Visual analytics with decision tree on network traffic flow for botnet detection
title_full Visual analytics with decision tree on network traffic flow for botnet detection
title_fullStr Visual analytics with decision tree on network traffic flow for botnet detection
title_full_unstemmed Visual analytics with decision tree on network traffic flow for botnet detection
title_sort visual analytics with decision tree on network traffic flow for botnet detection
publisher International Center for Scientific Research and Studies
publishDate 2018
url http://eprints.utm.my/id/eprint/86502/1/MuhammadKhairulRijal2018_VisualAnalyticswithDecisionTreeonNetwork.pdf
http://eprints.utm.my/id/eprint/86502/
http://home.ijasca.com/data/documents/4_Pg_72-91_Visual-Analytics-with-Decision-Tree-on-Network-Traffic-Flow-for-Botnet-Detection.pdf
_version_ 1680321055923634176
score 13.211869

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