Biologically Inspired Anomaly Detection for Hierarchical Wireless Sensor Networks

The resource constraint characteristic of sensor nodes make wireless sensor networks (WSN) very vulnerable to resource depletion attack such as DoS/DDos attack. On the other hand, the resource constraint characteristic also makes anomaly detection a challenging problem in WSN. To address the challenge, this paper presents an anomaly detection framework by taking the advantages of artificial immune system (AIS) and fuzzy theory. The proposed framework incorporates three components: local danger sensing, co-stimulation and global recognition. Due to the hierarchical structure and cooperative mechanism, the proposed model shows more advantages in detection performance than conventional method. The simulation results show that compared to watchdog method, the proposed method can provide higher detection rate and lower false detection rate. Moreover, the propose method shows advantages in flexibility and adaptability.