RFID-based human behavior modeling and anomaly detection for elderly care
Mobile Information Systems
RFID-based human behavior modeling and anomaly detection for elderly care
Mobile Information Systems
BSPNN: boosted subspace probabilistic neural network for email security
Artificial Intelligence Review
Enabling bring-your-own-device using mobile application instrumentation
IBM Journal of Research and Development
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Intrusion detection is the means to identify the intrusive behaviors and provides useful information to intruded systems to respond fast and to avoid or reduce damages. In recent years, learning machine technology is often used as a detection method in anomaly detection. In this research, we use support vector machine as a learning method for anomaly detection, and use LibSVM as the support vector machine tool. By using this tool, we get rid of numerous and complex operation and do not have to use external tools for finding parameters as need by using other algorithms such as the genetic algorithm. Experimental results show that high average detection rates and low average false positive rates in anomaly detection are achieved by our proposed approach.