Detection of Location Falsification Attacks in GPS driven Cloud-assisted Cognitive Radio Networks

Objective: The main objective of this method is to design a efficient mechanism to protect the cloud assisted cognitive radio networks from location falsification attacks. Theoretical Framework: The theoretical framework includes different techniques to find angle of arrival, time of arrival and signal strength and their applications in security. Methodology: Static spectrum policy has led to the inefficient way of using the spectrum resources. Dynamic spectrum access in cognitive radio was capable of solving the problem. In cognitive radio, the secondary users (SU) can use the white spaces in the spectrum opportunistically without creating any interference to the primary user (PU). Cloud-assisted cognitive radio is a renowned technique in cognitive radio for opportunistically using the spectrum. In this technique, SU has to report his exact location to the cloud database for getting the idle channels. GPS plays a crucial role in determining the location of the user which makes it fragile to attackers. If the attackers spoof the GPS to report false location, it will create harm to the PU. To resolve this issue, we proposed three-step mechanism that uses angle of arrival using MUSIC algorithm, time of arrival and signal strength for detecting the fake user. Field Significance: The significance of this research is to enable high security in cognitive radio networks and prevent interference to PU. Outcome: The outcome of this research is detecting the fake location reported by the attacker and simulating the scenarios using Matlab