Voice analysis system for detection of vishing using deep learning

Abstract

For many years, telecom fraud has caused significant financial harm to Indian telecommunications users. Traditional approaches for identifying telecom fraud frequently center on boycotting of faux phone numbers. Attackers, on the other hand, could merely escape discernment by modifying their phone numbers, which is fairly straightforward with VoIP (Voice over IP).To address this issue, this method detects telecommunication fraud supporting the substance of a spoken language rather than merely the caller's sign. This paper collects chronicles of telecommunication deceit, above all, from press sources and social platforms. To make datasets, our planned model utilizes machine learning techniques to look at knowledge and opt for high-quality descriptions from antecedently gathered knowledge. After that, Natural language processing is employed to draw out characteristics from the text- based data. Then, for extra telecommunication fraud detection, criteria to acknowledge identical material within the same call are formed. To spot telecommunication fraud online, the system provides an associate degree android application that will be loaded on a customer's smartphone. Once an associate degree incoming fraud decision is answered, the program will dynamically measure the call's contents to sight fraud. Our findings demonstrate that the system will effectively safeguard clients.