| Popis: |
Cognitive Radio Network (CRN) technology has offered the opportunistic solution for the spectrum scarcity problem in wireless communication. The Dynamic Spectrum Access (DSA) solution enables radio system to sense and learn the spectrum and reconfigure the parameters to apply cognitive decisions. With these properties, the technology is threatened by attackers and malicious users trying to exploit the network operation and its learning capabilities. Along with traditional threats, a few specific threats have been inadvertently \textit{created} by this technology due to its characteristic behavior and operation.This thesis provides a brief discussion on the threats and attacks with recent contributions on the security of CRN and proposes a security algorithm that uses the Artificial Neural Network (ANN) based machine learning methods to verify incumbent signals in a CRN. The proposed model is trained using Levenberg-Marquardt (LM) algorithm and Scaled Conjugate Gradient (SCG) algorithms to implement signal identification in two sub-categories, namely, known and unknown signals. Signal datasets were collected from the popular NASA Space Communications and Navigation (SCaN) testbed located at international space station (ISS) and also generated from a small in-lab Software Defined Radio (SDR) device to train and test the proposed model. The performances of the two algorithms on multiple datasets were compared using confusion matrices and mean squared error (MSE). Our study concluded that the best performing model exhibits MSE as low as 0.018 and the confusion matrix shows promising results of more than 98\% as the percentage of accurate prediction. The proposed model can be used in a CRN to monitor the signal activity of the users in the network and verify them for genuineness. The model can also alert the system when an unknown user is operating in the network for further security evaluations. |
