Paper title: Social engineering attack: A review of detection through machine learning algorithms
Prashansa Choksi, Chetan Shingadiyal, Nirav Bhatt, Hare Ram Singh · 2025
Machine learning (ML) algorithm plays a vital role in the prediction of Indian Stock Market to ( Vohra and Tanna, 2021 ) yoga poses ( Patel and Lathigara, 2022 ), and many other fields like social engineering attacks. Social engineering attacks are a serious risk to people’s, companies’, and information systems’ security. Adversaries increasingly rely on manipulating human behavior to gain unauthorized access to sensitive data as traditional security measures become more complex. Using ML algorithms, this research focuses on creating and implementing a reliable system for detecting social engineering attacks. A large dataset of social engineering attack patterns and associated safe user behaviors is used by the suggested system. To distinguish between harmful and benign activities, a range of ML techniques are investigated, including but not limited to supervised learning, unsupervised learning, and ensemble methods. A key component of improving the accuracy of the model is feature engineering, which involves carefully extracting and analyzing features like linguistic cues, response times, and communication patterns. Hence it provides a noteworthy advancement in tackling the escalating difficulties presented by social engineering assaults. With the help of ML, the suggested detection system offers a preventative measure against the increasingly cunning strategies used by malevolent actors.