A Regression Based Spectrum-Sensing Data - Falsification Attack Detection Technique in CWSN

CWSNs provide better bandwidth utilization as compared to a normal wireless sensor network because they use opportunistic spectrum access to transfer data. Opportunistic spectrum access is a very promising and spectrum efficient communication process when there is bur sty traffic in the network. IEEE 802.22 is the first standard based on the concept of cognitive radio. It also helps the network to eliminate collisions and delays in data delivery. While doing so, however, CWSNs are subject to several security threats, attacks on secrecy and authentication, attacks on network availability, stealthy attacks on service integrity etc. The attacks on network availability are known as the Denial of Service (DOS) attacks. The Spectrum Sensing Data Falsification (SSDF) attack is a type of DOS attack. Here the attackers modify the spectrum sensing report in order to compel the cognitive sensor node to take a wrong decision regarding the vacant spectrum band in other´s networks. In this paper we have proposed a new algorithm for dynamic spectrum sensing and spectrum allocation in Cognitive wireless sensor networks (CWSNs), the Maximum-Match Filtering algorithm (MMF). This algorithm is executed at the base station to counter the above attack.