Leveraging Naive Bayesian Machine Learning for Detecting Pig Butchering Scams: A Cybersecurity Social Engineering Perspective in Africa

Abstract- investment scams, particularly pig butchering scams, are a burgeoning global issue, particularly in Africa, resulting in substantial emotional and financial damage. Fraudsters frequently employ psychological manipulation and trust-based strategies, frequently through the use of social media, to entice individuals into fraudulent investment schemes. Criminals have increased their opportunities to exploit unsuspecting individuals in Africa as a result of the increasing prevalence of the internet. For example, Nigeria incurs an estimated $500 million in annual losses due to internet deception, while South Africa has experienced a 26% rise in scam complaints. This paper investigates the utilization of social engineering in these schemes and employs the Naive Bayesian machine learning algorithm to identify patterns. It underscores the capacity of machine learning to identify fraudulent behavior by analyzing communication patterns, financial anomalies, and dubious activities. The objective of the investigation is to employ data-driven methodologies to reduce frauds in Africa, suggest preventive measures, and increase awareness. Keywords: Pig Butchering Scam, Naïve Bayesian, Machine Learning, Cybersecurity, Financial Fraud