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The primary challenge faced over the course of this decade-long endeavour is the difficulty in devising effective features without direct access to telephony network infrastructure. we conducted an extensive three-month measurement study using these call logs, which encompassed a staggering 9 billion records. Based on the insights gleaned from this study, we identified and designed 29 features that could be used by machine learning algorithms to predict malicious calls. Fraudulent phone calls or scams and spam s via telephone or mobile phone have become a common threat to individuals and organizations. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as powerful tools in detecting and analyzing fraud or malicious calls. This paper presents an overview of AI-based fraud or spam detection and analysis techniques, along with its challenges and potential solutions. The novel fraud call detection approach is proposed that achieved high accuracy and precision. The outcomes revealed that the most effective approach could reduce unblocked malicious calls by up to 90%, while maintaining a precision rate exceeding 93.79% for benign call traffic. Moreover, our analysis demonstrated that these models could be implemented efficiently without incurring significant latency overhead.


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BHARGAVI, D. K. ., & SHIVANI, B. M. . (2024). DETECTION OF FRAUDULENT PHONE CALLS DETECTION IN MOBILE APPLICATIONS. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(2), 1–5.
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