Advanced Data Science Frameworks for Predictive Cyber-Risk Assessment and Adaptive Security Policy Optimization in Zero Trust Networks
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V1I4P108Keywords:
Cyber-Risk Assessment, Zero Trust Networks, Data Science, Machine Learning, Adaptive Security, Predictive Analytics, Policy Optimization, Network SecurityAbstract
The current digital infrastructures are more vulnerable to complex cyber threats and thus require intelligent, adaptive and predictive security systems. The paradigm of Zero Trust Networks (ZTNs) has become a future-prospective initiative as it removes the implicit trust and makes the continuous verification a prerequisite. Nevertheless, customary security paradigms in ZTNs do not usually have advanced predictive functionality and flexible policy enhancement fabrications. This paper will suggest a consolidated intensive data science-based system of predictive cyber-risk evaluation and adaptive optimization of the security policies in the context of zero risk settings. The framework that has been proposed utilizes machine learning, deep learning, statistical modeling, and optimization to forecast threats, measure the level of risks, and automatically update security policies. The study analyses the historical network traffic, behavioral analytics, and contextual intelligence on building predictive models related to cyber-risk forecasting. The feature engineering methods are also used to derive informative indicators within heterogeneous data accessing the user behavior records, system logs, and threat intelligence feeds. A reinforcement and un-reinforcement learning algorithms are being used to detect any anomalous patterns and attack vectors. Moreover, a combination of reinforcement learning and multi-objective optimization techniques is employed to change security policies according to changing threat-based scenarios and business needs. The paper outlines a data acquisition and preprocessing, predictive analytics, risk scoring, and policy optimization layers that make up the study modularity. Experimental analyses show that there are better detection accuracy, lower rates of false-positives, as well as higher response efficiency than traditional rule-based systems. The findings reveal that the suggested framework has up to 25 percent of risk in increased threat prediction accuracy and 18 percent of misconfigured policies decreased. This study adds value in the form of an analytical model that provides a link between the data science practices and the ideas of Zero Trust. The results outline how smart security coordination may enhance cyber resilience within mass enterprise and clouds. The solution proposed helps in the proactive defense methods, the improvement of situational awareness and the constant security adjustment in a dynamic network ecosystem.
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