A Conceptual Framework for Leveraging Artificial Intelligence in Proactive Threat Detection in Cybersecurity
Keywords:
Cybersecurity, Machine Learning, Deep Learning, Anomaly Detection, Threat Intelligence, Proactive Defense, AIAbstract
A conceptual framework is introduced, leveraging artificial intelligence (AI) techniques, including machine learning (ML) and behavioral analysis, to enable proactive threat detection in cybersecurity. This framework addresses the increasing complexity of modern cyber threats by integrating critical components such as anomaly detection, threat intelligence, user behavior analysis, and automated response systems. These components are designed to function collaboratively, providing an adaptive and resilient defense mechanism capable of detecting and mitigating a wide spectrum of cyber threats in real time.
Although the primary focus of this research is the theoretical development of the framework, it highlights the pivotal role of real-time threat intelligence integration in enhancing the system’s capacity to respond to emerging threats. This integration facilitates the creation of a dynamic and proactive defense strategy, positioning the framework as a viable solution for organizations aiming to enhance their cybersecurity posture in the face of an evolving threat landscape.
Future research will involve empirical validation of the framework in real-world environments, such as smart cities and enterprise networks, to assess its effectiveness and scalability. Key areas of investigation will include the efficiency of data processing, resilience to adversarial attacks, and the scalability of the model. This framework serves as a foundation for advancing AI-driven cybersecurity solutions, providing organizations with a robust mechanism to counter sophisticated and continuously evolving cyber threats.
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