An efficient approach for malicious base station and link failure detection in heterogeneous networks

Abstract

Next Generation Wireless Networks NGWN require the convergence and collaboration of different Radio Access Technologies RAT The combination or integration of different wireless networks is called Heterogeneous Wireless Network HWN In these networks the selection of the best radio access technology during the process of handoff plays a major role in the functionality of the whole network While the selection of a RAT is done the security factor also comes into role Though most of the existing methods employ different schemes to tackle security issues yet there are a lot of implications regarding malicious base station detection in heterogeneous networks and prior link failure analysis At the same time it is also necessary to balance traffic overloads during link failure detection So to resolve these issues three different techniques such as Co Active Neural Fuzzy Inference System CANFIS Connectivity factor analysis along with Genetic Algorithm and Adaptive Neural Fuzzy Inference system are used Detection of malicious Base Stations BS in heterogeneous wireless networks is important to improve the Quality of Service QoS in wireless networks The trust features Cumulative Binary Features and the Cost Index Features are extracted from the individual BS in different wireless network environment which differentiate the behavior of the normal BS from malicious BS in different networks using Co Active Neuro Fuzzy Inference System CANFIS classification approach newline