Authors: Professor Mayuri Dongre, Saurabh Bhoyar, Sanskar Karnewar
Abstract: Fraudulent voice calls have become a prominent cyber threat in the contemporary telecommunication environment as the usage of online banking, UPI transactions, mobile wallets, and instant messaging services becomes widespread. The perpetrators of cybercrime resort to fraudulent activities such as voice calls, phishing attacks, OTP manipulation, lottery scams, insurance scams, loan scams, and identity deception. The consequences include substantial monetary damage and grave security vulnerabilities. Existing techniques for spam detection in phone calls depend upon manual reporting, blacklisting, and basic rules-based filtering algorithms. However, these methods prove ineffective against newly emerging and evolving forms of fraud, particularly when the perpetrator changes their phone number and employs advanced social engineering techniques. Therefore, there is a need to develop an efficient and automated fraud detection system. In this paper, we propose a machine learning-based method to detect and analyze fraudulent phone calls. Using the following indicators for call behavior analysis, duration of a call, frequency of calls, suspicious phrases, time of calls, and voice pattern recognition, our approach is intended to identify and classify every call as either fraudulent or legitimate. For better prediction and detection, such machine learning models as Naive Bayes, Logistic Regression, Random Forest, and Support Vector Machine (SVM) will be applied.
