Hybrid AI Models in Network Security: Combining ML, DL, and Rule-Based Systems
Anitha Mareedu · International Journal of Emerging Research in Engineering and Technology · 2024
As cyber threats grow in sophistication, traditional rule-based and standalone machine learning (ML) approaches often fall short in ensuring robust network security. This review explores hybrid Artificial Intelligence (AI) models combinations of ML, deep learning (DL), and rule-based reasoning as a promising path for more adaptive and intelligent threat detection systems. The article analyzes how hybrid AI enhances predictive capabilities in network defense, especially when integrated into Security Information and Event Management (SIEM) systems and threat intelligence pipelines. We examine architectural designs such as ensemble models, federated hybrid learning, and AI-assisted policy engines. Through industry case studies including 5G telecom networks, financial fraud detection, and government threat infrastructures we illustrate how hybrid models outperform conventional systems in terms of detection accuracy, response time, and resilience to evasion techniques. The article also addresses critical governance and deployment issues, such as explainability, secure microservice communication, and policy integration in hybrid environments. Emerging research areas are highlighted, including quantum-secure hybrid frameworks, lightweight edge-compatible models, and privacy-preserving federated AI. Despite significant potential, current implementations face limitations in interpretability, scalability, and real-time processing under constrained resources. To address these, we propose practical recommendations for system architects and researchers, emphasizing modular design, auditing mechanisms, and ethical safeguards. This review offers a comprehensive overview of the evolution, benefits, and future of hybrid AI in network security, serving as a guide for both academic inquiry and practical implementation in dynamic threat landscapes