Optimised Levenshtein centroid cross-layer defence for multi-hop cognitive radio networks

Cognitive radio networks (CRN) make use of dynamic spectrum access to communicate opportunistically. Unlicensed users severely affect the spectrum sensing outcomes in CRN. Primary user emulation attack (PUEA) and spectrum sensing data falsification (SSDF) have become a paramount concern in CRNs. It is especially challenging when both masquerading (in the physical layer) and falsification (in the data link layer) occur by providing false spectrum reports. Existing methods to detect such attacks cannot be utilised in scenarios with multi-hop CRN. In this study, to mitigate attack against PUEA and SSDF, a method called optimised sensing and Levenshtein nearest centroid classification (OS-LNCC) for multi-hop CRN is presented. First, a network model for multi-hop CRN is designed. Next, a probable density optimal logical sensing model is designed to alleviate the problems related to falsification of spectrum reports. Here, the falsification of spectrum reports is overcome by exploiting dual factors, that is, probability for false alarm and probability for detection according to the departure rate of primary user (PU). With these dual factors, optimal logical sensing is made, therefore improving the throughput with minimum delay. Finally, each cognitive radio (CR) user evaluates its current sensing information to existing sensing classes through the Levenshtein distance function. Based on quantitative variables, the prediction function of each sensing class is measured using nearest centroid (NC) classifier and the sensing report is classified into either presence or absence of PU. These predictive classes are then integrated at the fusion centre so that robust mitigation against PUEA and SSDF is made. Computer simulation outputs show that OS-LNCC method performs better than the conventional methods using metrics such as sensing delay by 47%, percentage of error in prediction by 46% and throughput by 45%.