Intelligent Cybersecurity Models for Threat Detection and Prevention

Por Lip Yee

Intelligent Cybersecurity Models for Threat Detection and Prevention

Author(s)	Dr. Por Lip Yee
Country	Malaysia
Abstract	The rapid expansion of interconnected digital systems, cloud infrastructures, Internet of Things devices, enterprise platforms, and data-intensive applications has significantly increased the complexity of the contemporary cybersecurity environment. Conventional security mechanisms that primarily depend on predefined signatures and static rules are increasingly challenged by sophisticated malware, zero-day vulnerabilities, credential attacks, insider threats, automated attacks, and rapidly changing attack patterns. Intelligent cybersecurity models provide an opportunity to strengthen threat detection and prevention by combining machine learning, behavioral analytics, anomaly detection, automated intelligence, continuous monitoring, and adaptive security mechanisms. This study examines intelligent cybersecurity models for threat detection and prevention and proposes an integrated conceptual framework for strengthening organizational cyber resilience. A conceptual descriptive research design was adopted using secondary literature related to cybersecurity, artificial intelligence, machine learning, intrusion detection, anomaly detection, threat intelligence, behavioral analytics, endpoint security, network security, and automated incident response. The analysis identifies data collection, preprocessing, behavioral profiling, anomaly detection, threat classification, risk prioritization, automated response, continuous monitoring, and model refinement as major components of intelligent cybersecurity. The proposed framework integrates these components into a continuous security cycle beginning with data acquisition and ending with adaptive security improvement. The findings indicate that machine-learning-based models can identify complex relationships within security data and detect unusual activities that may not match predefined signatures. Behavioral analytics can strengthen detection by establishing baselines for normal user, network, and system activities. Threat intelligence can further improve detection by incorporating information about emerging indicators, attack techniques, vulnerabilities, and adversarial behaviors. However, intelligent cybersecurity models also face challenges involving false positives, adversarial manipulation, insufficient training data, model drift, explainability, privacy, computational requirements, and integration with legacy infrastructure.
Keywords	Intelligent Cybersecurity, Threat Detection, Threat Prevention, Machine Learning, Artificial Intelligence, Anomaly Detection, Behavioral Analytics, Cyber Threat Intelligence, Intrusion Detection, Cyber Resilience.
Field	Engineering
Published In	Volume 3, Issue 3, May-June 2021
Published On	2021-05-01

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American International Journal of Advanced Research and Development

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Intelligent Cybersecurity Models for Threat Detection and Prevention

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