Hybrid Machine Learning Models Combining Support Vector Machines and Deep Reinforcement Learning for Cyber Risk Profiling
Srinivasan M.L, S Surender, S Keerthana · 2025
The increasing complexity of cyber threats necessitates the development of advanced machine learning models that can efficiently detect, assess, and mitigate risks in realtime. Traditional machine learning approaches often struggle with evolving attack patterns, while deep learning models require extensive training data and computational resources. This book chapter explores a novel hybrid machine learning architecture that integrates Support Vector Machines (SVM) and Deep Reinforcement Learning (DRL) for cyber risk profiling. The hybrid approach leverages SVM’s superior classification capabilities and DRL’s adaptive decisionmaking to enhance cyber defense mechanisms against sophisticated attacks. Key aspects such as model architecture, feature engineering, computational efficiency, realtime adaptability, and attack surface reduction are systematically analyzed., advanced optimization techniques, including feature selection, model compression, parallel processing, and hardware acceleration, are explored to ensure scalability and realtime applicability in cybersecurity environments. Experimental evaluations and comparative analysis with traditional models demonstrate the superior performance of the hybrid SVMDRL framework in terms of accuracy, adaptability, and computational efficiency. The findings provide a comprehensive foundation for the next generation of AIdriven cybersecurity models, addressing the challenges of threat detection, risk assessment, and proactive cyber defense strategies.