Increasing Cybersecurity Threat Visibility with AI-Powered Network Analytics

Authors

  • Prof. Anders Bergman Faculty of Computer Science, Linköping University, Sweden Author

Keywords:

AI-powered network analytics, cybersecurity, threat visibility, machine learning

Abstract

Cybersecurity operations increasingly utilize modern technologies to detect and respond to complex cyberattacks. Traditional network monitoring and threat detection cannot identify modern threats. AI-powered network analytics may improve threat visibility, identification, and response. AI might enhance cybersecurity by enhancing network traffic visibility, anomaly detection, and threat prediction. The research uses real-time network monitoring and threat detection using machine learning, deep learning, and NLP. AI-powered network analytics' pros and cons for cybersecurity specialists are also covered. The study shows that cybersecurity AI requires refinement to fully integrate AI into security operations.

Downloads

Download data is not yet available.

References

Madupati, Bhanuprakash. "Data Science in Public Relations Software Development." Available at SSRN 5076688 (2022).

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 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

Pillai, Vinayak. Anomaly Detection for Innovators: Transforming Data into Breakthroughs. Libertatem Media Private Limited, 2022.

Gondaliya, Jayraj, et al. "Hybrid security RSA algorithm in application of web service." 2018 1st International Conference on Data Intelligence and Security (ICDIS). IEEE, 2018.

Talati, Dhruvitkumar. "Quantum minds: Merging quantum computing with next-gen AI." (2023).

Kalluri, Kartheek. "Migrating Legacy System to Pega Rules Process Commander v7. 1." (2015).

Madupati, Bhanuprakash. "Machine Learning for Cybersecurity in Industrial Control Systems (ICS)." Available at SSRN 5076696 (2022).

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.

Talati, Dhruvitkumar. "Artificial intelligence (AI) in mental health diagnosis and treatment." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.3 (2023): 251-253.

Kalluri, Kartheek. "ENHANCING CUSTOMER SERVICE EFFICIENCY: A COMPARATIVE STUDY OF PEGA'S AI-DRIVEN SOLUTIONS."

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

Madupati, Bhanuprakash. "Cybersecurity in Day-to-Day Life: A Technical Perspective." Available at SSRN 5076692 (2022).

Talati, Dhruvitkumar. "Telemedicine and AI in Remote Patient Monitoring." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.3 (2023): 254-255.

Singu, Santosh Kumar. "ETL Process Automation: Tools and Techniques." ESP Journal of Engineering & Technology Advancements 2.1 (2022): 74-85.

Kalluri, Kartheek. "Federate Machine Learning: A Secure Paradigm for Collaborative AI in Privacy-Sensitive Domains." International Journal on Science and Technolo-gy 13.4 (2022): 1-13.

Madupati, Bhanuprakash. "Cybersecurity in the Airline Industry: A Technical Perspective." Available at SSRN 5076684 (2022).

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.

Singu, Santosh Kumar. "Impact of Data Warehousing on Business Intelligence and Analytics." ESP Journal of Engineering & Technology Advancements 2.2 (2022): 101-113.

Kalluri, Kartheek. "Optimizing Financial Services Implementing Pega's Decisioning Capabilities for Fraud Detection." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 10.1 (2022): 1-9.

Talati, Dhruvitkumar. "AI in healthcare domain." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.3 (2023): 256-262.

Kalluri, Kartheek. "Blockchain Augment AI: Securing Decision Pipelines Decentralized in Systems."

Downloads

Published

29-12-2023

How to Cite

[1]
P. A. Bergman, “Increasing Cybersecurity Threat Visibility with AI-Powered Network Analytics”, American J Data Sci Artif Intell Innov, vol. 3, pp. 157–163, Dec. 2023, Accessed: Mar. 07, 2026. [Online]. Available: https://ajdsai.org/index.php/publication/article/view/57