A social network is a set of relationships and interactions among social entities such as individuals, organizations, and groups. The social network analysis is one of the major topics in the ongoing research field. The major problem regarding the social network is finding the most influential objects or persons. Identification of most influential nodes in a social network is a tedious task as large numbers of new users join the network every day. The most commonly used method is to consider the social network as a graph and find the most influential nodes by analyzing it. The degree centrality method is node based and has the advantage of easy identification of most influential nodes. In this paper a method called “Enhanced Degree Centrality Measure” is proposed which integrates clustering co-efficient value along with node based degree centrality. The enhanced degree centrality measure is applied to three different datasets which are obtained from the Facebook to analyze the performance. The response obtained is compared with existing methods such as degree centrality method and SPIN algorithm. By comparison, it is found that highest number of active nodes identified by the proposed method is 64 when compared with SPIN algorithm which identifies only 55.

Denial of Service (DoS) attacks are prevailing as a significant threat in computer networks necessitating a system to detect DoS attacks for protecting the computing resources. The existing solutions to detect DoS attacks face the lacuna of dimensionality which escalates computational cost and false alarm rate. These issues have been addressed by proposing Class Scatter Ratio (CSR) and Feature Distance Map (FDM) based statistical approach for detecting DoS attacks. CSR calculates the weights of each feature for identifying the best by distance based classifier. FDM extracts the correlation among the features. The attack is detected by comparing the computed FDM of new traffic with normal and attack profile vectors. Three experiments were conducted and the performance evaluation reveals that the computational complexity, false alarm rate, and execution time are low for the proposed approach. Further, it is evident from the ten fold cross validations that the accuracy obtained by the proposed approach for all the datasets are within the 95% confidence interval. Moreover, the proposed statistical approach yields significant results compared to the existing feature selection and extraction techniques and state-of-the-art attack detection systems.