Advanced Computational Techniques for Analyzing Cybersecurity Event Datasets Using Artificial Intelligence and Machine Learning
Neelam Kumari, Ashok Kumar · SCT Proceedings in Interdisciplinary Insights and Innovations. · 2025
Introduction: The complexity and range of cyber threats continue to grow, presenting challenges that traditional security approaches struggle to address. Objective: Artificial intelligence is transforming cybersecurity by empowering organizations to proactively combat threats through automated response mechanisms and predictive threat analysis. Leveraging data analytics, behavioral insights, and machine learning, AI-driven systems can forecast cyberattacks, enabling faster and more accurate threat detection. Method: By automating responses and addressing threats in real time, these systems reduce the risk of human error and enhance damage control. Result: Key AI techniques, including anomaly detection, predictive modeling, and real-time threat evaluation, are explored alongside considerations of data privacy, ethical concerns, and the potential dangers of adversarial attacks. The advantages and limitations of applying AI in cybersecurity are examined as well. Conclusion: This article provides a foundation for the future of intelligent and automated cyber defense strategies, showcasing how AI can reshape the cybersecurity landscape through practical examples and real-world case studies.