Adversarial AI in Social Engineering Attacks: Large- Scale Detection and Automated Counter measures
DOI:
https://doi.org/10.56127/ijst.v4i1.1964Keywords:
Social Engineering Attacks, Generative AI, Deepfake Technology, Phishing Detection, Behavioral Biometrics, Multi-modal AI Defense, Fraud Prevention, Natural Language Processing (NLP),, Transformer Models, Security in Digital Transactions, AI-driven CybersecurityAbstract
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.
References
1. Ai, L., Kumarage, T. S., Bhattacharjee, A., Liu, Z., Hui, Z., Davinroy, M. S., & Hirschberg, J. (2024). Defending against social engineering attacks in the age of LLMs. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 12880–12902.
2. Schmitt, M., & Flechais, I. (2024). Digital deception: Generative artificial intelligence in social engineering and phishing. Artificial Intelligence Review, 57, Article 324.
3. Alsmadi, I., Ahmad, K., Nazzal, M., Alam, F., Al-Fuqaha, A., Khreishah, A., & Algosaibi, A. (2021). Adversarial attacks and defenses for social network text processing applications: Techniques, challenges and future research directions. arXiv Preprint.
4. Faotu, H., Asheshemi, O. N., & Jeremiah, T. E. (2024). Human vulnerabilities in cybersecurity: Analyzing social engineering attacks and AI-driven machine learning countermeasures. Journal of Science and Technology, 30(1), 72–84.
5. Xu, H., Ma, Y., Liu, H., Deb, D., Liu, H., Tang, J., & Jain, A. K. (2019). Adversarial attacks and defenses in images, graphs and text: A review. arXiv Preprint.
6. Kumar, S., & Singh, R. (2024). AI-enabled phishing detection: A comprehensive survey. Journal of Cybersecurity and Privacy, 4(3), 45–68.
7. Lee, J., & Park, S. (2024). Machine learning approaches to detect social engineering attacks: A review. Computers & Security, 125, 102973.
8. Chen, Y., & Zhao, X. (2024). Deep learning techniques for phishing detection: A survey. IEEE Access, 12, 45678–45695.
9. Ahmed, M., & Khan, S. (2024). Generative adversarial networks for social engineering attack simulation. Information Processing & Management, 61(6), 103912.
10. Patel, R., & Desai, M. (2024). Behavioral biometrics for detecting social engineering attacks. Journal of Information Security and Applications, 70, 103215.
11. Wang, L., & Li, H. (2024). Natural language processing techniques for social engineering detection. Expert Systems with Applications, 213, 118912.
12. Gomez, A., & Torres, P. (2024). AI-driven countermeasures against phishing attacks: A systematic review. Computers & Security, 126, 102984.
13. Singh, A., & Verma, P. (2024). Social engineering attack detection using machine learning: Challenges and solutions. Journal of Cybersecurity, 10(2), 1–15.
14. Zhou, Y., & Wang, J. (2024). A survey on adversarial machine learning in cybersecurity. ACM Computing Surveys, 56(1), 1–36.
15. Khan, R., & Ali, M. (2024). AI-based detection of spear-phishing emails: A review. Journal of Information Security, 15(3), 123–140.
16. Nguyen, T., & Tran, D. (2024). Machine learning techniques for detecting social engineering attacks: A comprehensive survey. Information Security Journal: A Global Perspective, 33(2), 89–105.
17. Huang, C., & Lin, D. (2024). Detecting phishing websites using deep learning: A survey. IEEE Transactions on Neural Networks and Learning Systems, 35(4), 1234–1245.
18. Garcia, M., & Lopez, J. (2024). AI-based frameworks for social engineering attack detection: A review. Journal of Network and Computer Applications, 210, 103495.
19. Kim, H., & Choi, Y. (2024). A comprehensive survey on machine learning approaches for phishing detection. Journal of Information Science, 50(1), 45–67.
20. Rahman, M., & Islam, S. (2024). Social engineering attack detection using AI: A systematic literature review. Information & Computer Security, 32(2), 123–140.
21. Li, X., & Zhang, Y. (2024). Deep learning methods for detecting social engineering attacks: A review. Journal of Intelligent & Fuzzy Systems, 45(3), 345–360.
22. Chen, L., & Sun, Q. (2024). AI techniques for detecting phishing attacks: A comprehensive survey. Journal of Cybersecurity and Privacy, 4(4), 89–112.
23. Ahmed, S., & Rahman, A. (2024). Machine learning approaches for social engineering attack detection: A review. Journal of Information Security and Applications, 71, 103230.
24. Wang, Y., & Liu, Z. (2024). A survey on AI-based detection of social engineering attacks. Journal of Computer Virology and Hacking Techniques, 20(1), 1–20.
25. Zhao, H., & Li, W. (2024). LLM-based phishing detection: Challenges and solutions. Journal of AI Research, 67, 212–229.
26. Okafor, J., & Okoro, O. (2024). Deepfake audio and its implications in social engineering. African Journal of Cybersecurity, 10(4), 55–70.
27. Ramesh, S., & Kumar, T. (2024). Real-time AI countermeasures for phishing websites. Indian Journal of Computer Science, 22(2), 101–120.
28. Garcia, F., & Ramos, A. (2024). Adversarial examples in phishing and social engineering detection. Cybersecurity Advances, 9(3), 91–108.
29. Thompson, L., & Murphy, J. (2024). Human factors in AI-enhanced social engineering detection. Journal of Human-Centered Security, 18(1), 67–85.
30. Bashir, M., & Khan, A. (2024). Cross-domain detection of phishing using transformer-based models. International Journal of Cyber Threat Intelligence, 11(2), 33–52.
