Vol. 4 No. 2 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

Explainable Artificial Intelligence for Transparent Cybersecurity Decision-Making

PDF
  • John Smith
John Smith
PhD, Associate Professor, Department of Computer Science, Stanford University, Stanford, CA, USA

Published 05-10-2024

Keywords

  • Explainable Artificial Intelligence,
  • Cybersecurity
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
J. Smith, “Explainable Artificial Intelligence for Transparent Cybersecurity Decision-Making”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 2, pp. 106–113, Oct. 2024, Accessed: Dec. 22, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/179

Abstract

As cyber threats continue to evolve in complexity and frequency, the need for effective cybersecurity solutions has never been more critical. In this context, the integration of Explainable Artificial Intelligence (XAI) into cybersecurity systems presents a transformative opportunity to enhance transparency and trust in automated decision-making processes. This paper discusses the significance of explainability in AI-driven cybersecurity solutions, emphasizing how XAI can bridge the gap between technical efficacy and user trust. By examining various XAI models and their application in high-stakes cybersecurity scenarios, this research underscores the potential for XAI to improve the interpretability of decision-making processes, thereby fostering greater confidence among stakeholders. The findings suggest that the deployment of XAI in cybersecurity not only enhances operational effectiveness but also aligns with ethical considerations, promoting responsible AI usage in sensitive domains.

PDF

Downloads

Download data is not yet available.

References

  1. Vangoor, Vinay Kumar Reddy, et al. "Zero Trust Architecture: Implementing Microsegmentation in Enterprise Networks." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 512-538.
  2. Gayam, Swaroop Reddy. "Artificial Intelligence in E-Commerce: Advanced Techniques for Personalized Recommendations, Customer Segmentation, and Dynamic Pricing." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 105-150.
  3. Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Predictive Maintenance of Banking IT Infrastructure: Advanced Techniques, Applications, and Real-World Case Studies." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 86-122.
  4. Putha, Sudharshan. "AI-Driven Predictive Analytics for Maintenance and Reliability Engineering in Manufacturing." Journal of AI in Healthcare and Medicine 2.1 (2022): 383-417.
  5. Sahu, Mohit Kumar. "Machine Learning for Personalized Marketing and Customer Engagement in Retail: Techniques, Models, and Real-World Applications." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 219-254.
  6. Kasaraneni, Bhavani Prasad. "AI-Driven Policy Administration in Life Insurance: Enhancing Efficiency, Accuracy, and Customer Experience." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 407-458.
  7. Kondapaka, Krishna Kanth. "AI-Driven Demand Sensing and Response Strategies in Retail Supply Chains: Advanced Models, Techniques, and Real-World Applications." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 459-487.
  8. Kasaraneni, Ramana Kumar. "AI-Enhanced Process Optimization in Manufacturing: Leveraging Data Analytics for Continuous Improvement." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 488-530.
  9. Pattyam, Sandeep Pushyamitra. "AI-Enhanced Natural Language Processing: Techniques for Automated Text Analysis, Sentiment Detection, and Conversational Agents." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 371-406.
  10. Kuna, Siva Sarana. "The Role of Natural Language Processing in Enhancing Insurance Document Processing." Journal of Bioinformatics and Artificial Intelligence 3.1 (2023): 289-335.
  11. George, Jabin Geevarghese, et al. "AI-Driven Sentiment Analysis for Enhanced Predictive Maintenance and Customer Insights in Enterprise Systems." Nanotechnology Perceptions (2024): 1018-1034.
  12. P. Katari, V. Rama Raju Alluri, A. K. P. Venkata, L. Gudala, and S. Ganesh Reddy, “Quantum-Resistant Cryptography: Practical Implementations for Post-Quantum Security”, Asian J. Multi. Res. Rev., vol. 1, no. 2, pp. 283–307, Dec. 2020
  13. Karunakaran, Arun Rasika. "Maximizing Efficiency: Leveraging AI for Macro Space Optimization in Various Grocery Retail Formats." Journal of AI-Assisted Scientific Discovery 2.2 (2022): 151-188.
  14. Sengottaiyan, Krishnamoorthy, and Manojdeep Singh Jasrotia. "Relocation of Manufacturing Lines-A Structured Approach for Success." International Journal of Science and Research (IJSR) 13.6 (2024): 1176-1181.
  15. Paul, Debasish, Gunaseelan Namperumal, and Yeswanth Surampudi. "Optimizing LLM Training for Financial Services: Best Practices for Model Accuracy, Risk Management, and Compliance in AI-Powered Financial Applications." Journal of Artificial Intelligence Research and Applications 3.2 (2023): 550-588.
  16. Namperumal, Gunaseelan, Akila Selvaraj, and Yeswanth Surampudi. "Synthetic Data Generation for Credit Scoring Models: Leveraging AI and Machine Learning to Improve Predictive Accuracy and Reduce Bias in Financial Services." Journal of Artificial Intelligence Research 2.1 (2022): 168-204.
  17. Soundarapandiyan, Rajalakshmi, Praveen Sivathapandi, and Yeswanth Surampudi. "Enhancing Algorithmic Trading Strategies with Synthetic Market Data: AI/ML Approaches for Simulating High-Frequency Trading Environments." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 333-373.
  18. Pradeep Manivannan, Amsa Selvaraj, and Jim Todd Sunder Singh. “Strategic Development of Innovative MarTech Roadmaps for Enhanced System Capabilities and Dependency Reduction”. Journal of Science & Technology, vol. 3, no. 3, May 2022, pp. 243-85
  19. Yellepeddi, Sai Manoj, et al. "Federated Learning for Collaborative Threat Intelligence Sharing: A Practical Approach." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 146-167.
  20. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
  21. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
  22. S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd ed. Upper Saddle River, NJ, USA: Prentice Hall, 2010.
  23. C. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.
  24. D. Silver et al., “Mastering the game of Go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, pp. 484–489, 2016.
  25. Y. Bengio, “Learning deep architectures for AI,” Foundations and Trends in Machine Learning, vol. 2, no. 1, pp. 1–127, 2009.
  26. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 1097–1105.
  27. T. M. Mitchell, Machine Learning. New York, NY, USA: McGraw-Hill, 1997.
  28. G. Hinton, L. Deng, D. Yu, et al., “Deep neural networks for acoustic modeling in speech recognition,” IEEE Signal Process. Mag., vol. 29, no. 6, pp. 82–97, Nov. 2012.

Similar Articles

You may also start an advanced similarity search for this article.