Advanced Explainability Methods for AI-Based Cyber Forensics in Incident Investigation
Keywords:
AI-based systems, cyber forensics, explainability, interpretabilityAbstract
Artificial intelligence (AI) is increasingly integrated into cybersecurity operations, particularly in cyber forensics for incident investigation. However, one of the key challenges in the adoption of AI technologies for forensic analysis is the black-box nature of many machine learning models. This paper explores advanced explainability methods for AI-based systems in cyber forensics, focusing on how these methods enhance the interpretability of AI decisions during incident investigation. The paper discusses several explainability techniques such as Local Interpretable Model-agnostic Explanations (LIME), Shapley values, and counterfactual explanations, analyzing their application and effectiveness in cybersecurity contexts. Furthermore, it investigates how these techniques can be leveraged to provide transparent insights into AI decisions, thus fostering trust and ensuring accountability in forensic investigations. The research also highlights the challenges and limitations associated with implementing explainable AI (XAI) in cyber forensics, proposing potential solutions and future directions for research.
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References
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Proceedings of the 31st Conference on Neural Information Processing Systems, 4765–4774.
S. Kumari, “Kanban-Driven Digital Transformation for Cloud-Based Platforms: Leveraging AI to Optimize Resource Allocation, Task Prioritization, and Workflow Automation”, J. of Artificial Int. Research and App., vol. 1, no. 1, pp. 568–586, Jan. 2021
Singu, Santosh Kumar. "Designing scalable data engineering pipelines using Azure and Databricks." ESP Journal of Engineering & Technology Advancements 1.2 (2021): 176-187.
Madupati, Bhanuprakash. "Blockchain in Day-to-Day Life: Transformative Applications and Implementation." Available at SSRN 5118207 (2021).
S. Kumari, “Digital Transformation Frameworks for Legacy Enterprises: Integrating AI and Cloud Computing to Revolutionize Business Models and Operational Efficiency ”, Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, pp. 186–204, Jan. 2021
Singu, Santosh Kumar. "Real-Time Data Integration: Tools, Techniques, and Best Practices." ESP Journal of Engineering & Technology Advancements 1.1 (2021): 158-172.
S. Kumari, “Kanban and AI for Efficient Digital Transformation: Optimizing Process Automation, Task Management, and Cross-Departmental Collaboration in Agile Enterprises”, Blockchain Tech. & Distributed Sys., vol. 1, no. 1, pp. 39–56, Mar. 2021
Madupati, Bhanuprakash. "Kubernetes: Advanced Deployment Strategies-* Technical Perspective." (2021).
Zhang, X., Chen, Q., & He, Y. (2021). Counterfactual explanations in deep learning: Applications to cybersecurity. Journal of Cybersecurity Technology, 5(3), 110-121.
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretability. Proceedings of the 2017 ICML Workshop on Human Interpretability in Machine Learning.
Zhang, Z., & Yang, L. (2020). Bias in machine learning models: Understanding and mitigating its impact on forensic analysis. International Journal of Cyber Forensics, 4(2), 88–98.
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., & specter, M. (2018). Explaining explanations: An approach to analyzing explanations of machine learning. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1-11.
Kaur, H., & Kumar, M. (2022). Hybrid model-based explainability for AI in cybersecurity. Journal of Cybersecurity Research, 12(3), 42-53.
Xie, L., & Liu, X. (2021). Domain adaptation in cybersecurity AI models. International Conference on Machine Learning, 295-305.
Millar, M. (2020). Legal and ethical implications of explainable AI in cybersecurity forensics. Journal of Information Privacy and Security, 16(4), 21-36.
