Ms. Samhita Kuili Profile

Samhita Kuili

at University of Ottawa

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 31 May 2022 Presentation + Paper

A holistic machine learning approach to identify performance anomalies in enterprise WiFi deployments

Samhita Kuili, Burak Kantarci, Melike Erol-Kantarci, Marcel Chenier, Bernard Herscovici

Proceedings Volume 12097, 120970C (2022) https://doi.org/10.1117/12.2621087

KEYWORDS: Feature selection, Machine learning, Data modeling, Detection and tracking algorithms, Visualization, Local area networks, Data communications, Clouds, Binary data, Standards development

Read Abstract +

In large-scale enterprise WiFi deployments, performance anomalies need to be detected autonomously without human intervention. The state of the art in detection and diagnosis of faults is a manual and slow process. Experts with domain-knowledge use alarms, traces, and Key Performance Indicator (KPI) data to understand the anomalies. In large-scale deployments, this is becoming an increasingly inefficient approach. The objective of this study is to determine anomalies in a WiFi network by exploiting big data, machine learning (ML) and deep learning techniques. Our work comprises of setting up WiFi access points and retrieving the KPI of client devices which are connected to access points. The metrics for client devices, access points and channel nodes are periodically logged on the cloud, and collected through an API. We develop a multivariate time series data which undergoes data preprocessing, such as data cleaning and data transformation prior to using ML algorithms. To detect anomalous instances, an unsupervised algorithm, Density Based Spatial Clustering of Applications with Noise (DBSCAN) is used. Clusters obtained through DBSCAN are analyzed under dimensionality reduction technique t-Distributed Stochastic Neighbor Embedding. While DBSCAN helps to obtain the outliers, the output of the dimensionality reduction technique enable us to coherently comprehend the dynamics of clusters. Our methodology involves a thorough analysis of the outliers from the dataset through legacy supervised and self-supervised ML models, as well as ensemble learning techniques.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years