ESP Journal of Engineering & Technology Advancements |
© 2022 by ESP JETA |
Volume 2 Issue 4 |
Year of Publication : 2022 |
Authors : Saidaiah Yechuri |
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Saidaiah Yechuri, 2022. "Role of AI with Authentication and Authorization for High Throughput Applications", ESP Journal of Engineering & Technology Advancements, 2(4): 142-147.
As the use of Artificial Intelligence in various domains continues to grow, there is an increasing need to ensure the trustworthiness and responsible deployment of these systems (Barclay & Abramson, 2021). Trustworthy AI is essential as AI applications become more pervasive and their impact on society becomes more significant. A key aspect of trustworthy AI is the need to identify the roles, requirements, and responsibilities of the various stakeholders involved in the development and deployment of AI systems. With the rapid advancements in artificial intelligence and machine learning, there has been a growing interest in leveraging these technologies for various applications, including security and authentication. This research paper explores the state-of-the-art in AI-assisted authentication and authorization, and discusses the opportunities, challenges, and future directions in this domain.
[1] Al-Qaraghuli, G Z. (2022, April 24). AI-Assisted Authentication: State of the Art, Taxonomy and Future Roadmap. https://export.arxiv.org/pdf/2204.12492v1.pdf
[2] Azmoodeh, A., & Dehghantanha, A. (2022, January 1). Deep Fake Detection, Deterrence and Response: Challenges and Opportunities. Cornell University. https://doi.org/10.48550/arXiv.2211.
[3] Barclay, I., & Abramson, W. (2021, September 21). Identifying Roles, Requirements and Responsibilities in Trustworthy AI Systems. https://doi.org/10.1145/3460418.3479344
[4] Czeskis, A., Dietz, M., Kohno, T., Wallach, D S., & Balfanz, D. (2012, October 15). Strengthening user authentication through opportunistic cryptographic identity assertions. https://doi.org/10.1145/2382196.2382240
[5] Dilek, S., Çakır, H., & Aydın, M. (2015, January 31). Applications of Artificial Intelligence Techniques to Combating Cyber Crimes: A Review. , 6(1), 21-39. https://doi.org/10.5121/ijaia.2015.6102
[6] Habibpour, M., Gharoun, H., Mehdipour, M., Tajally, A., Asgharnezhad, H., Shamsi, A., Khosravi, A., & Nahavandi, S. (2023, April 14). Uncertainty-aware credit card fraud detection using deep learning. Elsevier BV, 123, 106248-106248. https://doi.org/10.1016/j.engappai.2023.106248
[7] Mohammed, A H Y., Dziyauddin, R A., & Latiff, L A. (2023, January 1). Current Multi-factor of Authentication: Approaches, Requirements, Attacks and Challenges. Science and Information Organization, 14(1). https://doi.org/10.14569/ijacsa.2023.0140119
[8] Neupane, S., Fernandez, I., Mittal, S., & Rahimi, S. (2023, January 1). Impacts and Risk of Generative AI Technology on Cyber Defense. Cornell University. https://doi.org/10.48550/arxiv.2306.13033
[9] Sarker, I H., Furhad, M H., & Nowrozy, R. (2021, March 26). AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions. Springer Nature, 2(3). https://doi.org/10.1007/s42979-021-00557-0
[10] Sarker, I H., Janicke, H., Mohammad, N., Watters, P., & Nepal, . (2023, January 1). AI Potentiality and Awareness: A Position Paper from the Perspective of Human-AI Teaming in Cybersecurity. Cornell University. https://doi.org/10.48550/arXiv.2310.
[11] Servos, D., & Osborn, S L. (2017, January 2). Current Research and Open Problems in Attribute-Based Access Control. Association for Computing Machinery, 49(4), 1-45. https://doi.org/10.1145/3007204
[12] Truong, T C., Diep, Q B., & Zelinka, I. (2020, March 4). Artificial Intelligence in the Cyber Domain: Offense and Defense. Multidisciplinary Digital Publishing Institute, 12(3), 410-410. https://doi.org/10.3390/sym12030410
[13] Zhu, G., & Al-Qaraghuli, Y. (2022, January 1). AI-Assisted Authentication: State of the Art, Taxonomy and Future Roadmap. Cornell University. https://doi.org/10.48550/arXiv.2204.
AI, Authorizations, Applications.