Performance improvement in vertical heterogeneous handoff methodology using CANFIS classification approaches

Abstract

Summary Detection of malicious Base Stations (BS) in heterogeneous wireless networks is significant for developing an efficient system in wireless networks. In this paper, the handoff is performed between two different networks such as Wi‐Fi and WiMax, which may be located in different locations in the same region. The trust features, Cumulative Binary Features and Cost Index Features of each BS in different wireless network environment, are derived individually, which differentiates the behavior of the normal BS and malicious BS in different networks using Co‐Active Neuro Fuzzy Inference System (CANFIS) classification approach. The proposed methodology is compared with other state‐of‐arts methods for evaluating the individuality and efficiency. The proposed heterogeneous handoff methodology achieves 96.02% of packet delivery ratio, 90% detection rate, 94.6% of precision, and 15.99 ms of latency.