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 |