On Augmented Intelligence and Performance Anomaly Detection in Unlabeled OpenWiFi Data

Performance degradation of OpenWiFi traffic is a significant problem while provisioning service to a dense area consisting of thousands of clients operating at the same time. Among various vulnerabilities of poor performance in Wireless Local Area Networks (WLANs), deviation of a traffic pattern from normal indicates probable presence of anomaly or outlier. Adoption of machine learning algorithms including unsupervised and supervised models, the complexity of detection of a anomalous traffic along with respective root cause is untangled with augmented machine learning to involve domain knowledge input. In this paper, to cope with unlabeled data in an OpenWiFi setting, the following systematic work flow is proposed to augment machine learning-based anomaly detection. First, a combination of two unsupervised clustering algorithms is used to segregate the anomalies from normal distribution of traffic. The anomalous instances are confirmed via domain knowledge input. Next, supervised models are trained to detect anomalies in a different domain (Wireless Sensor Networks) with labeled data. Following upon feature extraction to obtain the same number of dimensions in the OpenWiFi data as the labeled dataset, trained supervised classifiers are used to detect anomalies in the OpenWiFi system. Furthermore, the impact of oversampling methods have also been investigated. Through numerical results we show the impact of the proposed supervised models in terms of decision-making metrics and the shift in performances from the standpoint of imbalanced distribution of rare-class classification problem.