Anomaly detection in smart homes provides support to enhance the health and safety of people who live alone.Compared to the previous studies done on this topic,less attention has been given to hybrid methods.This pape...Anomaly detection in smart homes provides support to enhance the health and safety of people who live alone.Compared to the previous studies done on this topic,less attention has been given to hybrid methods.This paper presents a two-steps hybrid probabilistic anomaly detection model in the smart home.First,it employs various algorithms with different characteristics to detect anomalies from sensory data.Then,it aggregates their results using a Bayesian network.In this Bayesian network,abnormal events are detected through calculating the probability of abnormality given anomaly detection results of base methods.Experimental evaluation of a real dataset indicates the effectiveness of the proposed method by reducing false positives and increasing true positives.展开更多
This paper presents a new method for soft error detection using software redundancy (SEDSR) that is able to detect transient faults. Soft errors damage the control flow and data of programs and designers usually use h...This paper presents a new method for soft error detection using software redundancy (SEDSR) that is able to detect transient faults. Soft errors damage the control flow and data of programs and designers usually use hardware-based solutions to handle them. Software-based techniques for soft error detection force less cost and delay to systems and do not change their configuration. Therefore, these kinds of methods are appropriate alternatives for hardware-based techniques. SEDSR has two separate parts for data and control flow errors detection. Fault injection method is used to compare SEDSR with previous methods of this field based on the new parameter of “Evaluation Factor” that takes in account fault coverage, memory and performance overheads. These parameters are important in real time safety critical applications. Experimental results on SPEC2000 and some traditional benchmarks of this field show that SEDSR is much better than previous methods of this field. SEDSR’s evaluation factor is about 50% better than other methods of this field. These results show its success in satisfaction of the existing tradeoff between fault coverage, performance and memory overheads.展开更多
文摘Anomaly detection in smart homes provides support to enhance the health and safety of people who live alone.Compared to the previous studies done on this topic,less attention has been given to hybrid methods.This paper presents a two-steps hybrid probabilistic anomaly detection model in the smart home.First,it employs various algorithms with different characteristics to detect anomalies from sensory data.Then,it aggregates their results using a Bayesian network.In this Bayesian network,abnormal events are detected through calculating the probability of abnormality given anomaly detection results of base methods.Experimental evaluation of a real dataset indicates the effectiveness of the proposed method by reducing false positives and increasing true positives.
文摘This paper presents a new method for soft error detection using software redundancy (SEDSR) that is able to detect transient faults. Soft errors damage the control flow and data of programs and designers usually use hardware-based solutions to handle them. Software-based techniques for soft error detection force less cost and delay to systems and do not change their configuration. Therefore, these kinds of methods are appropriate alternatives for hardware-based techniques. SEDSR has two separate parts for data and control flow errors detection. Fault injection method is used to compare SEDSR with previous methods of this field based on the new parameter of “Evaluation Factor” that takes in account fault coverage, memory and performance overheads. These parameters are important in real time safety critical applications. Experimental results on SPEC2000 and some traditional benchmarks of this field show that SEDSR is much better than previous methods of this field. SEDSR’s evaluation factor is about 50% better than other methods of this field. These results show its success in satisfaction of the existing tradeoff between fault coverage, performance and memory overheads.
