Data driven analysis of Wi-Fi networks

Please use this identifier to cite or link to this item: http://repository.iiitd.edu.in/xmlui/handle/123456789/745

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DC Field	Value	Language
dc.contributor.author	Kesar, Devishi
dc.contributor.author	Kaul, Sanjit Krishnan (Advisor)
dc.contributor.author	Maity, Mukulika (Advisor)
dc.date.accessioned	2019-10-07T05:56:45Z
dc.date.available	2019-10-07T05:56:45Z
dc.date.issued	2018-11-26
dc.identifier.uri	http://repository.iiitd.edu.in/xmlui/handle/123456789/745
dc.description.abstract	With the increase in demand for enterprise wi- , there is a growing need to understand the functioning of these networks. Large scale Wi-Fi deployment is a challenging task. There is a need to diagnose network related problems after deployment of this large scale Wi-Fi. Another important task is to identify if the client behaviour is anomalous. Clustering is a method that can be used to detect it. In this report user trajectories have been trained using Doc2Vec and the results have been reported.	en_US
dc.language.iso	en_US	en_US
dc.publisher	IIITD-Delhi	en_US
dc.subject	Enterprise Network	en_US
dc.subject	Wi-Fi	en_US
dc.subject	Wireshark Data Analysis	en_US
dc.subject	Trajectory Data Mining	en_US
dc.subject	Clus- tering	en_US
dc.subject	Anomaly Detection	en_US
dc.title	Data driven analysis of Wi-Fi networks	en_US
dc.type	Other	en_US
Appears in Collections:	Year-2018

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