Given a large sparse graph, how can we find patterns and anomalies? Several important applications can be modeled as large sparse graphs, e.g., network traffic monitoring, resea...
Numerous temporal inference tasks such as fault monitoring and anomaly detection exhibit a persistence property: for example, if something breaks, it stays broken until an interve...
The state of the art is explored in using soft computing (SC) methods for network intrusion detection, including the examination of efforts in ten specific areas of SC as well as ...
Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract and label a person’s activities and signi...
Distributed sensor networks are highly prone to accidental errors and malicious activities, owing to their limited resources and tight interaction with the environment. Yet only a...
Claudio Basile, Meeta Gupta, Zbigniew Kalbarczyk, ...