An Optimized Fraud Identification Framework Using DCNN For Wireless Communication

Convolution Neural Network (Cnn) Is The Powerful Framework To Solve The Lot Of Issues Of Link Detection In Unlabeled Wireless Communication. Last Few Decades Lot Of Fraud Detection Strategies Has Been Projected Bit That All Are Inefficient For Detecting The Frauds. Therefore, The Great Need For Effective And Fast Fraud Detection Scheme With Higher Detection Accuracy. In This Research, Spider Based Convolution Neural Network (Sbcnn) Model To Detect The Frauds In The Wireless Communication. Initially, Create The Wireless Channel To Transmit The Messages From Source To Destination.  Here, The Fraudulent Activities Are Detected Based On The Packet Delivery Time Of The Source To Destination Of The Wireless Medium. Moreover, The Proposed System Implementation Is Done In The Matlab Frame Work Additionally; The Obtained Results Are Validated With Prevailing Methods For Evaluating The Efficiency Of The Proposed Sbcnn Approach. Wireless communication fraud poses a significant threat as unauthorized use of services, compromising the security of cellular networks and infrastructure. With the surge in online services and users, the reliance on wireless communication for high-speed internet applications has grown. Despite the convenience brought by technologies like net banking, credit cards, and online services, financial frauds and unauthorized payments remain substantial risks. The intricate nature of wireless communication, illustrated in Fig. 1, where devices use signals between source and destination nodes, leads to challenges like network interference and loss rate, often exacerbated by fraudulent activities. Numerous techniques, including Artificial Intelligence (AI), hybrid ensemble models, oversampling, and machine learning, have been explored but haven't provided satisfactory solutions. In this study, an optimization-assisted intelligent framework is proposed to maximize communication performance, addressing the limitations of existing approaches. The subsequent sections analyse related research, identify issues with conventional methods, elaborate on the functioning of the proposed framework, discuss results, and conclude with research inferences