Automatic Network Traffic Anomaly Detection and Analysis using SupervisedMachine Learning Techniques

Automatic Network Traffic Anomaly Detection and Analysis using SupervisedMachine Learning Techniques

Bibliographic Details
Main Author: Syal, Astha
Language:English
Published: Youngstown State University / OhioLINK 2019
Subjects:
Computer Science
Information Technology
Mining
Machine Learning
Data Science
Network Traffic Monitoring and Analysis
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=ysu1578259840945109
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-ysu15782598409451092021-08-03T07:13:46Z Automatic Network Traffic Anomaly Detection and Analysis using SupervisedMachine Learning Techniques Syal, Astha Computer Science Information Technology Mining Machine Learning Data Science Network Traffic Monitoring and Analysis Today, internet has become an important tool for the entire public. It is the source of information, education, entertainment, and convenience. To maintain the efficiency and performance of the large computer networks supporting the internet, it is important to monitor and analyze the overall network traffic. During evening hours, when most people access internet at the same time for social media browsing, accessing their data or watching Netflix, with the increase in utilization, the network traffic can become congested and therefore the speed decreases. This research aims to identify network variables that cause these disturbances, thus impacting the overall speed of the network and leading it to a state of "congestive collapse". Machine learning models can be built using data passively collected in the network’s logs and can be used in real-time to predict the traffic in the next time frame so network administrators could tune the network variables that are causing these disturbances. The models proposed here are able to quickly detect large intervals of low performing network transfers, which requires attention from network engineers. 2019 English text Youngstown State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ysu1578259840945109 http://rave.ohiolink.edu/etdc/view?acc_num=ysu1578259840945109 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Computer Science
Information Technology
Mining
Machine Learning
Data Science
Network Traffic Monitoring and Analysis
spellingShingle Computer Science
Information Technology
Mining
Machine Learning
Data Science
Network Traffic Monitoring and Analysis
Syal, Astha
Automatic Network Traffic Anomaly Detection and Analysis using SupervisedMachine Learning Techniques
author Syal, Astha
author_facet Syal, Astha
author_sort Syal, Astha
title Automatic Network Traffic Anomaly Detection and Analysis using SupervisedMachine Learning Techniques
title_short Automatic Network Traffic Anomaly Detection and Analysis using SupervisedMachine Learning Techniques
title_full Automatic Network Traffic Anomaly Detection and Analysis using SupervisedMachine Learning Techniques
title_fullStr Automatic Network Traffic Anomaly Detection and Analysis using SupervisedMachine Learning Techniques
title_full_unstemmed Automatic Network Traffic Anomaly Detection and Analysis using SupervisedMachine Learning Techniques
title_sort automatic network traffic anomaly detection and analysis using supervisedmachine learning techniques
publisher Youngstown State University / OhioLINK
publishDate 2019
url http://rave.ohiolink.edu/etdc/view?acc_num=ysu1578259840945109
work_keys_str_mv AT syalastha automaticnetworktrafficanomalydetectionandanalysisusingsupervisedmachinelearningtechniques
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