TIM-8350 v5

Sharma, A., & Babbar, H. (2024). Detecting Cyber threats in real-time: a supervised learning perspective on the CTU-13 dataset [Conference paper]. 2024 5th International Conference for Emerging Technology (INCET), 1–5. https://doi.org/10.1109/INCET61516.2024.10593100
This paper leverages the CTU-13 dataset to develop machine learning-based intrusion detection models capable of accurately identifying and classifying benign and malicious network behavior.

Fang, Q., Weilong, T., & Lvjun, Z. (2024). Research on detecting unknown cyber security threats in power systems using data mining technology [Conference paper]. 2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA), Image Processing and Computer Applications (ICIPCA), 196–200. https://doi.org/10.1109/ICIPCA61593.2024.10708987
This article explores how data mining techniques can enhance cybersecurity in digital power enterprises by detecting unknown threats and addressing limitations of traditional methods against malicious files and unpredictable cyber risks.

Khoury, J., Nader, C., Pour, M. S., & Bou-Harb, E. (2025). An internet-scale data-driven approach for exploring cyber threats amid global conflicts. IEEE Internet of Things Magazine, 8(1), 66–72. https://doi.org/10.1109/IOTM.001.2400044
This article presents a data-driven approach to analyzing global cyber threats, using Internet-wide empirical data sources to detect, measure, and characterize cyber threat dynamics across geopolitical domains.

Mitra, S., Neupane, S., Chakraborty, T., Mittal, S., Piplai, A., Gaur, M., & Rahimi, S. (2024). LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge.
This article introduces LocalIntel, an automated threat intelligence framework that uses large language models to contextualize global cyber threats with local organizational knowledge for effective SoC response.

Dorothy, A. B., Madhavidevi, B., Nachiappan, B., Manikandan, G., Patjoshi, P. K., & Sindhuja, M. (2024). AI-driven threat intelligence in cloud computing detecting and responding to cyber attacks [Conference paper]. 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS), 1–6. https://doi.org/10.1109/IACIS61494.2024.10721888
This research introduces an AI-driven cloud threat intelligence system that surpasses static rule-based methods by using anomaly detection and machine learning to adapt to evolving cyber threats.

Knight, A. (2024). When you can’t afford to miss: Likelihood of success and discrimination in cyberwarfare. The Review of Faith & International Affairs, 22(2), 61–72. https://doi.org/10.1080/15570274.2024.2335070
Some scholars challenge offense dominance in cyberspace, noting cyberattacks are slow, costly, risk indiscriminate harm, and may inspire future attacks through weapon exposure. This article looks at the impact of the case involving Russian CyberOps in Ukraine, and the historical cases such as Stuxnet and others, detailing the Just War Theory’s likelihood of success and discrimination doctrines.

Yigit, Y., Ferrag, M. A., Ghanem, M. C., Sarker, I. H., Maglaras, L. A., Chrysoulas, C., Moradpoor, N., Tihanyi, N., & Janicke, H. (2025). Generative AI and LLMs for critical infrastructure protection: Evaluation benchmarks, agentic AI, challenges, and opportunities. Sensors (14248220), 25(6), 1666. https://doi.org/10.3390/s25061666

Sambucci, L., & Paraschiv, E. (2024). The accelerated integration of artificial intelligence systems and its potential to expand the vulnerability of the critical infrastructure, Romanian Journal of Information Technology and Automatic Control (ISSN 1220-1758)34(3), 131-148. https://doi.org/10.33436/v34i3y202410
This paper examines the shifting threat environment surrounding AI systems, emphasizing how nation-state actors might exploit AI vulnerabilities to advance geopolitical interests. It analyzes various adversarial methods, such as data poisoning, model theft, and privacy inference attacks, and evaluates their possible effects on critical sectors like energy, transportation, healthcare, and water infrastructure.

Cruz, J. de A. da, & Pedron, S. (2020). Cyber mercenaries: A new threat to national security. International Social Science Review, 96(2), 1b.
In this article, the author provides a novel view of cyber mercenaries.

Narender, C., Shobana, J., Manasa, K., Revathi, M.P., Syed, R., & Jansirani, D. (2025). Cyber-physical systems security protecting critical infrastructure from cyber threats and attacks. ITM Web of Conferences, 76, 03005.
This paper introduces a scalable, cost-efficient, AI-driven adaptive security framework that integrates real-time threat monitoring with blockchain-based protection and machine learning-powered intrusion detection. The study contributes a practical and economically sustainable cybersecurity approach to enhance preparedness for future cyberattacks.

Safitra, M. F., Lubis, M., & Fakhrurroja, H. (2023). Counterattacking cyber threats: A framework for the future of cybersecurity. Sustainability (2071-1050), 15(18), 13369.
This article highlights the critical role of leadership, accountability, and innovation in building cyber resilience. As cyber threats grow more complex, the framework provides strategic direction for navigating the evolving relationship between digital transformation and cybersecurity, aiming to strengthen future digital security.

Dobrovolska, O., & Rozhkova, M. (2024). Development of the country’s sustainable cyberspace strategy to ensure the country’s national security. SocioEconomic Challenges, 8(2), 197–214. https://doi.org/10.61093/sec.8(2).197-214.2024
This article evaluates the nation’s sustainable cyberspace strategy by comparing it with cybersecurity approaches of economically advanced countries that leverage cutting-edge information and communication technologies.

Ali, A. D., Sofir, B., Sulaiman, S. F., Hamid, N. K., & Wassif, S. J. D. (2024). Regulating economic law and privacy through big data technologies [Conference paper]. 2024 International Conference on Emerging Research in Computational Science (ICERCS), 1–6. https://doi.org/10.1109/ICERCS63125.2024.10895428
In this paper, the authors detail the implications of how privacy law and data governance impact national security policy, global cooperation, and cybersecurity frameworks.