@inproceedings {42480,
	title = {RCAD:Real-time Collaborative Anomaly Detection System for Mobile Broadband Networks},
	journal = {ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
	year = {2022},
	pages = {2682{\textendash}2691},
	publisher = {ACM},
	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.\&nbsp; 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.\&nbsp; It also involves a collaborative distributed learning module that facilitates knowledge sharing across the system.\&nbsp; We implement and evaluate RCAD on real world measurements from a commercial mobile network.\&nbsp; RCAD achieves over 0.7 F-1 score significantly outperforming the state-of-the-art methods.\&nbsp;},
	author = {Azza Hassan Moh {Ahmed} and Riegler, Michael and Hicks, Steven and Elmokashfi, Ahmed}
}
