XG Boost Algorithm for Fraudulent Vishing Detection: A Review Literature - Laukika Nilangekar; Dnyaneshwari Popat Funde; Vaishnavi Kshatri; Jalindar Gandal (Vidhyayana - An International Multidisciplinary Peer-Reviewed E-Journal - ISSN 2454-8596, 2023)
Vishing is a growing concern in the age of digital technology, with scammers using voice and phone calls to trick individuals into revealing sensitive personal information. Traditional methods of detecting vishing scams involve manual analysis and reporting, which can be time-consuming and ineffective. The paper reviews notable examples of vishing scams, including the Microsoft Tech Support Scam, the IRS Impersonation Scam, the Jamaican Lottery Scam, and the Social Security Scam. It also discusses the increasing number of reported scam calls related to the COVID-19 pandemic. The paper outlines the key challenges in detecting vishing scams and the potential benefits of using AI and ML techniques. It concludes by highlighting the need for greater awareness and vigilance among individuals to protect their personal information. The prevalence of vishing scams poses a significant threat to personal information security, as cybercriminals use social engineering tactics to deceive victims and steal their personal and financial information. Traditional methods of detecting vishing scams are often ineffective and time-consuming, but they can be improved by artificial intelligence and machine learning techniques. Imposter scams are a common type of scam call, and with the rising number of reported scams calls in recent years, it is crucial to remain cautious when receiving unsolicited calls and avoid providing personal or payment information to unknown callers. The COVID-19 pandemic has resulted in an increase in scam calls related to the virus, underscoring the importance of awareness and necessary precautions to safeguard personal information. By utilizing AI and ML techniques, vishing scams can be detected more effectively, reducing the likelihood of falling victim to cybercriminals.