AuthorsA. H. M. Ahmed, M. Riegler, S. Hicks and A. Elmokashfi
TitleRCAD:Real-time Collaborative Anomaly Detection System for Mobile Broadband Networks
AfilliationCommunication Systems, Machine Learning
Project(s)The Center for Resilient Networks and Applications, Department of Holistic Systems
StatusAccepted
Publication TypeProceedings, refereed
Year of Publication2022
Conference NameACM SIGKDD Conference on Knowledge Discovery and Data Mining
Abstract

The rapid increase in mobile data traffic and the number of connected devices and applications in networks is putting a significant pressure on the current network management approaches that heavily rely on human operators. Consequently, an automated network management system that can efficiently predict and detect anomalies is needed.  In this paper, we propose, RCAD, a novel distributed architecture for detecting anomalies in network data forwarding latency in an unsupervised fashion. RCAD employs the hierarchical temporal memory (HTM) algorithm for the online detection of anomalies.  It also involves a collaborative distributed learning module that facilitates knowledge sharing across the system.  We implement and evaluate RCAD on real world measurements from a commercial mobile network.  RCAD achieves over 0.7 F-1 score significantly outperforming the state-of-the-art methods. 

Citation Key42480