Unmasking Deception: Artificial Neural Networks in Smishing Detection for Cyber Security Fortification

Abstract

Smishing is a form of social engineering that employs text messaging to mislead people into revealing confidential information or performing malicious actions. It has become a prevalent and sophisticated cyber threat. The rise of smishing (SMS phishing) poses a significant threat to cyber security, demanding advanced detection and classification methods to safeguard users and organizations. As a deceptive technique targeting mobile users through text messages, smishing requires proactive defense mechanisms. This paper proposes an Artificial Neural Networks (ANNs) model for smishing detection and classification to bolster cyber security. This paper outlines a proposed framework comprising four distinct modules designed for smishing detection and classification. Through simulation, the framework demonstrated exceptional performance, attaining an accuracy rate of 97.66%. Comparative analysis against various machine learning models underscored the superiority of the proposed approach. As mobile devices continue to be integral to daily communication, the implementation of ANN-based solutions serves as a vital component in fortifying cyber defenses, ensuring the security and privacy of individuals and organizations in an increasingly interconnected digital landscape.