A self-adaptive evolutionary negative selection approach for home anomaly events detection

In this study, we apply the self-adaptive evolutionary negative selection approach for home abnormal events detection. The negative selection algorithm, also termed the exhaustive detector generating algorithm, is for various anomaly detection problems, and the concept originates from artificial immune system. Regarding the home abnormal control rules as the detector, we apply fuzzy genetic algorithm for self-adaptive information appliances control system, once the environment factors change. The proposed approach can be adaptive and incremental for the home environment factor changes. Via implementing the proposed approach on the abnormal temperature detection, we can make the information appliance control system more secure, adaptive and customized.