Multi-Layer Cyber Attack Response with Digital Forensics
Keywords:
AI-powered forensics, digital forensics, machine learning, cyber-attacksAbstract
Modern threads and multilayer intrusions are very persistent for that we need intermediate security breach detection, investigation, and response. Traditional digital forensic methods are not effective against cyber warfare’s, speed and complexity. Artificial intelligence can automate and enhance the performance of multidimensional cyber threat response. In this research paper we explore how AI powered digital forensic system quickly detect analyses React to multi layered cyber threads.
Downloads
References
Branz, M., & Nguyen, S. (2021). Machine learning for rapid cyberattack detection. International Journal of Information Security, 29(1), 45-60.
Chen, H., & Zhang, L. (2022). The role of AI in cybersecurity forensics. Cybersecurity Journal, 18(3), 215-225.
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
Sivaraman, Hariprasad. (2020). Integrating Large Language Models for Automated Test Case Generation in Complex Systems.
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-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
S. Kumari, “Kanban and Agile for AI-Powered Product Management in Cloud-Native Platforms: Improving Workflow Efficiency Through Machine Learning-Driven Decision Support Systems”, Distrib Learn Broad Appl Sci Res, vol. 5, pp. 867–885, Aug. 2019
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
Sivaraman, Hariprasad. (2020). Intelligent Deployment Orchestration Using ML for Multi-Environment CI/CD Pipelines.
S. Kumari, “AI-Powered Cybersecurity in Agile Workflows: Enhancing DevSecOps in Cloud-Native Environments through Automated Threat Intelligence ”, J. Sci. Tech., vol. 1, no. 1, pp. 809–828, Dec. 2020.
S. Kumari, “Cloud Transformation and Cybersecurity: Using AI for Securing Data Migration and Optimizing Cloud Operations in Agile Environments”, J. Sci. Tech., vol. 1, no. 1, pp. 791–808, Oct. 2020.
Singu, Santosh Kumar. "Designing scalable data engineering pipelines using Azure and Databricks." ESP Journal of Engineering & Technology Advancements 1.2 (2021): 176-187.
Sivaraman, Hariprasad. (2021). INTELLIGENT AUTOMATION FOR SERVICE DEGRADATION PREDICTION USING LLMS AND OBSERVABILITY DATA. International Journal of Engineering Management. 6. 10.5281/zenodo.14342920.
S. Kumari, “AI-Powered Cloud Security for Agile Transformation: Leveraging Machine Learning for Threat Detection and Automated Incident Response ”, Distrib Learn Broad Appl Sci Res, vol. 6, pp. 467–488, Oct. 2020
Kumar, P., & Singh, A. (2019). Machine learning applications in cybersecurity. Journal of Cyber Defense, 10(4), 170-180.
Li, Z., & Wang, J. (2020). AI-driven anomaly detection in cybersecurity. Journal of Digital Forensics, 5(2), 205-213.