A Systematic Review of Social Engineering Attacks & Techniques: The Past, Present, and Future

Social engineering, a prevalent cybercrime tactic, employs deceptive techniques to extract sensitive information, manipulating human decision-making. This study provides an in-depth review of social engineering attacks, countermeasures, challenges, and future trends. Utilizing the PRISMA methodology, a systematic literature review was conducted across diverse databases, resulting in the selection of 59 articles from an initial pool of 1,020. The findings reveal the adoption of various strategies, including machine learning, education, topic blacklisting, logo identification, visual similarities, search engine-based techniques, and identity management, to mitigate social engineering threats. Notably, machine learning emerges as the most utilized method (43.8%), followed by education and awareness programs (18.8%), highlighting their efficacy, scalability, adaptability, and cost-effectiveness in addressing this cybersecurity menace. Machine learning algorithms effectively identify patterns indicative of social engineering attacks, while educational initiatives empower users to recognize and thwart such tactics, thereby reducing susceptibility to manipulation and enhancing organizational resilience.