A Novel Anomaly Detection Algorithm Using DBSCAN and SVM in Wireless Sensor Networks

Anomaly is an important and influential element in Wireless Sensor Networks that affects the integrity of data. On account of the fact that these networks cannot be supervised, this paper, therefore, deals with the problem of anomaly detection. First, the three features of temperature, humidity, and voltage are extracted from the network traffic. Then, network data are clustered using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. It also analyzes the accuracy of DBSCAN algorithm input data with the help of density-based detection techniques. This algorithm detects the points in regions with low density as anomaly. By using normal data, it trains support vector machine. And, finally, it removes anomalies from network data. The proposed algorithm is evaluated by the standard and general data set of Intel Berkeley Research lab (IRLB). In this paper, we could obliterate DBSCAN's problem in selecting input parameters by benefiting from coefficient correlation. The advantage of the proposed algorithm over previous ones is in using soft computing methods, simple implementation, and improving detection accuracy through simultaneous analysis of those three features.