AI-driven threat intelligence for real-time cybersecurity: Frameworks, tools, and future directions
Kelvin Ovabor, Ismail Oluwatobiloba Sule-Odu, Travis Atkison, Adetutu Temitope Fabusoro, Joseph Oluwaseun Benedict · Open Access Research Journal of Science and Technology · 2024
AI-driven threat intelligence is transforming cybersecurity by enhancing real-time threat detection, analysis, and response capabilities. This paper reviews state-of-the-art AI frameworks, machine learning models, and tools that support threat intelligence, providing a survey of current research in the field and identifying challenges and future directions for real-time cybersecurity. Techniques such as supervised and unsupervised learning, reinforcement learning, and natural language processing (NLP) contribute to the robustness of threat detection, while evolving frameworks and ethics guide AI implementation in security operations. By addressing the increasing sophistication of cyber threats, AI-driven approaches aim to create a proactive, dynamic cybersecurity posture that can keep up with evolving cyber adversaries.