Analyzing Social Engineering Attack Patterns Using Behavioral Psychology and AI-Driven Defense Mechanisms

Complex Social Engineering (SE) attacks by manipulating people psychologically to mislead them and to weaken the security systems have become quite common. This research investigates the patterns of SE attacks through concepts of behavioral psychology integrated with protection frameworks driven by AI (Artificial Intelligence). This study evaluates significant weaknesses in the behaviour of human beings which hackers use against them by analysing the strategies of attackers such as gathering data, phishing techniques and exploitation of trust. We provide an enhanced protection architecture that includes BERT (Bidirectional Encoder Representations from Transformers) to deal with such attacks. Through the evaluation of text semantics in messages, emails, and websites, BERT's deep learning (DL) ability helps them detect phishing content, suspicious patterns of language, and fraudulent messages. Our approach decreases false positives and enhances contextual knowledge thereby improving traditional models used for detection. Experimental outcomes prove that BERT is more accurate in identifying harmful content than conventional Machine Learning (ML) approaches. This research focuses on the fact that the knowledge of behavioral psychology and AI-driven approaches can make cybersecurity systems more efficient and decrease the risk of SE attacks.