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

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

doi:10.1117/12.2621087

31 May 2022 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, Big Data IV: Learning, Analytics, and Applications; 120970C (2022) https://doi.org/10.1117/12.2621087
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

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.

Conference Presentation

Citation Download Citation

Samhita Kuili, Burak Kantarci, Melike Erol-Kantarci, Marcel Chenier, and Bernard Herscovici "A holistic machine learning approach to identify performance anomalies in enterprise WiFi deployments", Proc. SPIE 12097, Big Data IV: Learning, Analytics, and Applications, 120970C (31 May 2022); https://doi.org/10.1117/12.2621087

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