Mitigating Social Engineering Attacks Using Machine Learning and Artificial Intelligence
1Dahunsi Samuel Adeyemi
1Graduate Researcher: B.Sc. Telecommunications Science, MS Cyber security and Information Assurance, University of Central Missouri, Missouri, United States
Abstract
This study examined how social engineering attacks can be mitigated using machine learning and artificial intelligence. Secondary data was collected for this study from previously published literature, which was based on the inclusion and exclusion criteria identified by the researcher. Using specified inclusion and exclusion criteria, only five (5) articles were selected for the study from different databases. The study revealed that AI and machine learning models have proven highly effective in detecting and preventing various forms of social engineering, with phishing emerging as the most commonly used attack method. AI-driven algorithms are capable of analyzing large volumes of data to identify patterns of malicious behavior, such as scam messages or suspicious social media posts, thereby reducing the risk of security breaches. The study recommends that individuals should actively engage in cybersecurity education, making use of free online resources and training courses to stay informed about the latest social engineering tactics. Also, the study recommends that future studies should look into how artificial intelligence and machine learning can be utilized in preventing social engineering attacks, with a specific focus on data privacy efficacy.
Keywords: Cyber security, Artificial Intelligence, Social Engineering, Machine Learning, Cyber Threat Prevention, Information Assurance
Mitigating-Social-Engineering-Attacks-Using-Machine-Learning-and-Artificial-Intelligence