Intelligent Risk Assessment and Optimization Frameworks for Strengthening Organizational Network Security

References

[1] R. Gupta and P. Srivastava, "Artificial intelligence and machine learning in cyber security applications," in Cyber Security Solutions for Protecting and Building the Future Smart Grid, D. Asija, R. Viral, R. Das and G. Tuna, Eds., Elsevier, 2025, pp. 271-296.

[2] M. Danish and M. M. Siraj, "AI and Cybersecurity: Defending Data and Privacy in the Digital Age," Journal of Engineering and Computational Intelligence Review, vol. 3, no. 1, pp. 25-35, May 2025.

[3] K. Dhanushkodi and S. Thejas, "AI Enabled Threat Detection: Leveraging Artificial Intelligence for Advanced Security and Cyber Threat Mitigation," IEEE Access, vol. 12, pp. 173127-173136, 2024.

[4] I. Zografopoulos, J. Ospina, X. Liu and C. Konstantinou, "Cyber-Physical Energy Systems Security: Threat Modeling, Risk Assessment, Resources, Metrics, and Case Studies," IEEE Access, vol. 9, pp. 29775-29818, February 2021.

[5] J. Jacobs, S. romanosky, B. Edwards, I. Adjerid and M. Roytman, "Exploit Prediction Scoring System (EPSS)," Digital Threats: Research and Practice, vol. 2, no. 3, pp. 1-17, July 2021.

[6] F. H. Alshammari, "Design of capability maturity model integration with cybersecurity risk severity complex prediction using bayesian- based machine learning models," Service Oriented Computing and Applications, vol. 12, no. 1, pp. 59-72, March 2023.

[7] T. Zhukabayeva, A. Pervez, Y. Mardenov, M. Othman, N. Karabayev and Z. Ahmad, "A Traffic Analysis and Node Categorization- Aware Machine Learning-Integrated Framework for Cybersecurity Intrusion Detection and Prevention of WSNs in Smart Grids," IEEE Access, vol. 12, pp. 91715-91733, July 2024.

[8] F. R. Alzaabi and A. Mehmood, "A Review of Recent Advances, Challenges, and Opportunities in Malicious Insider Threat Detection Using Machine Learning Methods," IEEE Access, vol. 12, no. 1, pp. 30907-30927, February 2024.

[9] M. A. Hossain and M. S. Islam, "Enhanced detection of obfuscated malware in memory dumps: a machine learning approach for advanced cybersecurity.," Cybersecurity, vol. 7, no. 1, p. 16, 2024.

[10] A. Yeboah-Ofori, S. Islam, S. W. Lee, Z. U. Shamszaman, K. Muhammad and M. Altaf, "Cyber Threat Predictive Analytics for Improving Cyber Supply Chain Security," IEEE Access, vol. 9, pp. 94318-94337, June 2021.

[11] A. Tanikonda, B. K. Pandey, S. R. Peddinti and S. R. Katragadda, "Advanced AI-Driven Cybersecurity Solutions for Proactive Threat Detection and Response in Complex Ecosystems," Journal of Science & Technology, vol. 3, no. 1, pp. 196-217, January 2022.

[12] Z. Zhang, H. A. Hamadi, E. Damiani, C. Y. Yeun and F. Taher, "Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research," IEEE Access, vol. 10, no. 1, pp. 93104-93139, September 2022.

@article{1715068,
      author = {Dr. Deepak Tomar, Dr. Kismat Chhillar},
      title = {Intelligent Risk Assessment and Optimization Frameworks for Strengthening Organizational Network Security},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {9},
      pages = {787-794},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/17150681.pdf},
      abstract = {The increasing complexity and frequency of cyber threats have made intelligent risk assessment and optimization frameworks essential components of organizational network security. This study explores the integration of artificial intelligence and machine learning to enhance predictive capabilities, automate decision processes, and improve the precision of risk management strategies. By employing AI-driven risk scoring models and optimization algorithms, organizations can identify, prioritize, and mitigate vulnerabilities more effectively, achieving a balance between proactive defense and resource efficiency. The proposed framework emphasizes continuous adaptive learning, allowing systems to evolve with changing threat environments while maintaining transparency through explainable AI. Optimization techniques facilitates dynamic allocation of resources, ensuring compliance and resilience across complex network infrastructures. The findings underscore the transformative potential of AI-based optimization and risk assessment solutions in establishing more robust and agile defenses against increasingly sophisticated cyber risks in contemporary digital ecosystems.},
      keywords = {Artificial intelligence, machine learning, risk assessment, network security, optimization, explainable AI, adaptive defense},
      month = {March},
      doi = {https://doi.org/10.64388/IREV9I9-1715068}
  }