Subjective Logic-Based Anomaly Detection Framework in Wireless Sensor Networks

In existing anomaly detection approaches, sensor node often turns to neighbors to further determine whether the data is normal while the node itself cannot decide. However, previous works consider neighbors' opinions being just normal and anomalous, and do not consider the uncertainty of neighbors to the data of the node. In this paper, we propose SLAD (subjective logic based anomaly detection) framework. It redefines opinion deriving from subjective logic theory which takes the uncertainty into account. Furthermore, it fuses the opinions of neighbors to get the quantitative anomaly score of the data. Simulation results show that SLAD framework improves the performance of anomaly detection compared with previous works.