A GEP-Based Anomaly Detection Scheme in Wireless Sensor Networks

Trusted and reliable wireless sensor networks (WSNs) rely on the accurate and rapid detection of anomalies. However, the security model in wired networks is not suited for WSNs because of their energy constraints. In this paper, a traffic prediction model based on gene expression programming (GEP-ADS) is proposed, to predict the time series of normal traffic. Then we present a lightweight anomaly detection scheme (ADS) in WSNs. In the ADS, no more cooperation between sensor nodes is required, which dramatically decrease the energy consumption. Another key advantage of our approach is that GEP-ADS could solve the problem that the traditional time series methods can´t make an accurate prediction without the pre-knowledge. In order to evaluate our ADS, we have simulated several routing attacks, and fully discussed the parameter setting influence on our ADS. The simulation results show that our ADS is able to achieve high detection accuracy with a low false positive rate.