Fuzzy Sets and Systems - Special issue on nuclear engineering
Design of adaptive fuzzy controls based on natural control laws
Fuzzy Sets and Systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Communications of the ACM
An Immunological Approach to Change Detection: Theoretical Results
CSFW '96 Proceedings of the 9th IEEE workshop on Computer Security Foundations
A self-adaptive evolutionary negative selection approach for home anomaly events detection
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
An emergency model of home network environment based on genetic algorithm
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Designing secure sensor networks
IEEE Wireless Communications
Anomaly detection in wireless sensor networks
IEEE Wireless Communications
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Anomaly detection in sensor network seems a challenge when encountering the limitation of the energy requirement and dynamics environments. It is to rapidly analyze and identify the abnormal events among the extreme volume data. Using correlation graph representation to correlate the events generated by sensor networks is capable to find the intentional dependency behavior's insight for detecting home sensor network abnormal events. In this study, we proposed an anomaly detection mechanism based on correlation graphs of sensor networks for rapidly identifying abnormal home events. The proposed mechanism which makes the following contributions: (a) it is automatically identify the abnormal event under home sensor network environment (b) it eliminates irrelevant events for saving the computation power (c) it is easily to apply on different machine learning classifiers for enhancement. The evaluation from Intel Berkeley Research lab sensor network data set. The proposed mechanism performs well in sensor events elimination and abnormal event detection.