Predicting Attack Surface Effects on Attack Vectors in an Open Congested Network Transmission Session by Machine Learning

<p>This paper examined the impact of a network attack on a congested transmission session. The research is motivated by the fact that the previous research community has neglected to evaluate security issues related to network congestion environments, and has instead concentrated on resolving...

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Autor principal:	Nahla Aljojo
Formato:	article
Lenguaje:	EN
Publicado:	International Association of Online Engineering (IAOE) 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/65625e1ea37e48a99b4c8df427619f4e
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spelling	oai:doaj.org-article:65625e1ea37e48a99b4c8df427619f4e2021-11-16T07:23:28ZPredicting Attack Surface Effects on Attack Vectors in an Open Congested Network Transmission Session by Machine Learning2626-849310.3991/ijoe.v17i11.25025https://doaj.org/article/65625e1ea37e48a99b4c8df427619f4e2021-11-01T00:00:00Zhttps://online-journals.org/index.php/i-joe/article/view/25025https://doaj.org/toc/2626-8493<p>This paper examined the impact of a network attack on a congested transmission session. The research is motivated by the fact that the previous research community has neglected to evaluate security issues related to network congestion environments, and has instead concentrated on resolving congestion issues only. At any point in time, attackers can take advantage of the congestion problem, exploit the attack surface, and inject attack vectors. In order to circumvent this issue, a machine learning algorithm is trained to correlate attack vectors from the attack surface in a network congestion signals environment with the value of decisions over time in order to maximise expected attack vectors from the attack surface. Experimental scenario that dwell on transmission rate overwhelming transmission session, resulting in a standing queue was used. The experiment produced a dataset in which a TCP transmission through bursting transmission were capture. The data was acquired using a variety of experimental scenarios. Nave Bayes, and K-Nearest Neighbours prediction analyses demonstrate strong prediction performance. As a result, this study re-establishes the association between attack surface and vectors with network attack prediction. </p>Nahla AljojoInternational Association of Online Engineering (IAOE)articleComputer applications to medicine. Medical informaticsR858-859.7ENInternational Journal of Online and Biomedical Engineering, Vol 17, Iss 11, Pp 47-59 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Nahla Aljojo Predicting Attack Surface Effects on Attack Vectors in an Open Congested Network Transmission Session by Machine Learning
description	<p>This paper examined the impact of a network attack on a congested transmission session. The research is motivated by the fact that the previous research community has neglected to evaluate security issues related to network congestion environments, and has instead concentrated on resolving congestion issues only. At any point in time, attackers can take advantage of the congestion problem, exploit the attack surface, and inject attack vectors. In order to circumvent this issue, a machine learning algorithm is trained to correlate attack vectors from the attack surface in a network congestion signals environment with the value of decisions over time in order to maximise expected attack vectors from the attack surface. Experimental scenario that dwell on transmission rate overwhelming transmission session, resulting in a standing queue was used. The experiment produced a dataset in which a TCP transmission through bursting transmission were capture. The data was acquired using a variety of experimental scenarios. Nave Bayes, and K-Nearest Neighbours prediction analyses demonstrate strong prediction performance. As a result, this study re-establishes the association between attack surface and vectors with network attack prediction. </p>
format	article
author	Nahla Aljojo
author_facet	Nahla Aljojo
author_sort	Nahla Aljojo
title	Predicting Attack Surface Effects on Attack Vectors in an Open Congested Network Transmission Session by Machine Learning
title_short	Predicting Attack Surface Effects on Attack Vectors in an Open Congested Network Transmission Session by Machine Learning
title_full	Predicting Attack Surface Effects on Attack Vectors in an Open Congested Network Transmission Session by Machine Learning
title_fullStr	Predicting Attack Surface Effects on Attack Vectors in an Open Congested Network Transmission Session by Machine Learning
title_full_unstemmed	Predicting Attack Surface Effects on Attack Vectors in an Open Congested Network Transmission Session by Machine Learning
title_sort	predicting attack surface effects on attack vectors in an open congested network transmission session by machine learning
publisher	International Association of Online Engineering (IAOE)
publishDate	2021
url	https://doaj.org/article/65625e1ea37e48a99b4c8df427619f4e
work_keys_str_mv	AT nahlaaljojo predictingattacksurfaceeffectsonattackvectorsinanopencongestednetworktransmissionsessionbymachinelearning
_version_	1718426607220162560

Predicting Attack Surface Effects on Attack Vectors in an Open Congested Network Transmission Session by Machine Learning

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