Adversarial AI in Social Engineering Attacks: Large- Scale Detection and Automated Counter measures - Anil Kumar Pakina; Deepak Kejriwal; Tejaskumar Dattatray Pujari (Asosiasi Dosen Muda Indonesia, 2025)
Social engineering attacks using AI-generated deepfake information leverage rare cybersecurity threat hunting. Conventional phishing detection and fraud prevention systems are failing to catch detection errors due to AI-generated social engineering in email, voice, and video content. To mitigate the increased risk of AI-driven social engineering attacks, a new multi-modal AI defense framework, incorporating Transfer Learning through pre-trained language models, deep fake sound analysis, and behavior-analysis systems capable of pinpointing AI generated social engineering attack, is presented. Benefiting from the utilization of state-of-the-art deepfake voice recognition systems and behavior anomaly detector system (BADS) base for cash withdrawals, the discoverers show that the defense mechanism achieves unprecedented detection accuracy with the least incidence of false positives. This brings about the necessity for fraud prevention augmenting AI measures and provision of automated protection mitigating adversarial social engineering within the enterprise security and financial transaction systems.