Sixth-generation (6G) wireless networking studies have begun with the global implementation of fifth-generation (5G) wireless systems. It is predicted that multiple heterogeneity applications and facilities may be supported by modern wireless communication networks (MWCNs) with improved effectiveness and protection. Nevertheless, a variety of trust-related problems that are commonly disregarded in network architectures prevent us from achieving this objective. In the current world, MWCN transmits a lot of sensitive information. It is essential to protect MWCN users from harmful attacks and offer them a secure transmission to meet their requirements. A malicious node causes a major attack on reliable data during transmission. Blockchain offers a potential answer for confidentiality and safety as an innovative transformative tool that has emerged in the last few years. Blockchain has been extensively investigated in several domains, including mobile networks and the Internet of Things, as a feasible option for system protection. Therefore, a blockchain-based modal, Transaction Verification Denied conflict with spurious node (TVDCSN) methodology, was presented in this study for wireless communication technologies to detect malicious nodes and prevent attacks. In the suggested mode, malicious nodes will be found and removed from the MWCN and intrusion will be prevented before the sensitive information is transferred to the precise recipient. Detection accuracy, attack prevention, security, network overhead, and computation time are the performance metrics used for evaluation. Various performance measures are used to assess the method’s efficacy, and it is compared with more traditional methods.
One kind of telecom crime known as "traffic pumping" occurs when local phone companies artificially overstate the volume of calls flowing into their systems so that they may charge the calling party a greater access fee than their own. Lacking labels for training set makes it difficult to determine whether congestion pumps has occurred. In this study, we suggested a decision-support system based on cluster analysis and decision trees for identifying fraudulent cases. In this study, we use the IBM Telco and Cell2cell datasets. The gathered information can be preprocessed using normalization. When we have collected enough data, we use the rehabilitation frog leaping algorithm (RFLA) to divide up the possible incidents of fraud into distinct groups. Next, we used the cluster participation labels to build a decision tree that led us to the criteria that must be met in order to pursue legal action against the circumstances that raised red flags. Professionals in the field of telecommunications (TC) verify these guidelines in an effort to find a legal remedy against accused offenders. The results are demonstrated and proved the efficiency of the proposed system by comparing it with the conventional techniques.
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