Biblio

With the improvement in technology and with the increase in the use of wireless devices there is deficiency of radio spectrum. Cognitive radio is considered as the solution for this problem. Cognitive radio is capable to detect which communication channels are in use and which are free, and immediately move into free channels while avoiding the used ones. This increases the usage of radio frequency spectrum. Any wireless system is prone to attack. Likewise, the main two attacks in the physical layer of cognitive radio are Primary User Emulation Attack (PUEA) and replay attack. This paper focusses on mitigating these two attacks with the aid of authentication tag and distance calculation. Mitigation of these attacks results in error free transmission which in turn fallouts in efficient dynamic spectrum access.

Missing data problem often occurs in data analysis. The most common way to solve this problem is imputation. But imputation methods are only suitable for dealing with a low proportion of missing data, when assuming that missing data satisfies MCAR (Missing Completely at Random) or MAR (Missing at Random). In this paper, considering the reasons for missing data, we propose a novel support vector machine method using a new kernel function to solve the problem with a relatively large proportion of missing data. This method makes full use of observed data to reduce the error caused by filling a large number of missing values. We validate our method on 4 data sets from UCI Repository of Machine Learning. The accuracy, F-score, Kappa statistics and recall are used to evaluate the performance. Experimental results show that our method achieve significant improvement in terms of classification results compared with common imputation methods, even when the proportion of missing data is high.