An Efficient Multilevel Probabilistic Model for Abnormal Traffic Detection in Wireless Sensor Networks.

Wireless sensor networks (WSNs) are low-cost, special-purpose networks introduced to resolve various daily life domestic, industrial, and strategic problems. These networks are deployed in such places where the repairments, in most cases, become difficult. The nodes in WSNs, due to their vulnerable nature, are always prone to various potential threats. The deployed environment of WSNs is noncentral, unattended, and administrativeless; therefore, malicious attacks such as distributed denial of service (DDoS) attacks can easily be commenced by the attackers. Most of the DDoS detection systems rely on the analysis of the flow of traffic, ultimately with a conclusion that high traffic may be due to the DDoS attack. On the other hand, legitimate users may produce a larger amount of traffic known, as the flash crowd (FC). Both DDOS and FC are considered abnormal traffic in communication networks. The detection of such abnormal traffic and then separation of DDoS attacks from FC is also a focused challenge. This paper introduces a novel mechanism based on a Bayesian model to detect abnormal data traffic and discriminate DDoS attacks from FC in it. The simulation results prove the effectiveness of the proposed mechanism, compared with the existing systems.