Predictive Computational Models for Self-Adaptive Cybersecurity in Intelligent Networked Systems
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I1P102Keywords:
Predictive Computational Models, Self-Adaptive Cybersecurity, Intelligent Networked Systems, Anomaly Detection, Automated Threat Response, Machine Learning, Deep LearningAbstract
The growing nature of interconnectivity and complexity of intelligent networked systems has rendered conventional approaches to cybersecurity inadequate in safeguarding critical infrastructures. The present paper aims at providing a detailed work on the predictive computational systems that are used in self-adaptive cybersecurity of smart networked systems. Based on advanced machine learning, deep learning, and probabilistic models, these regimens predict the possibility of impending cyber threats, dynamically modify security systems, and mitigate threats in real-time. Some of the key issues that are addressed in the research include threat detection, anomaly prediction, automated response generation, and the enhancement of system resilience. The simulation findings show that predictive models are critical in acceleration and accuracy in detection and response of threats, which lowers the vulnerability of the system and maximises efficiency in the operations. Moreover, the paper suggests a structure on how to combine these models within the current network management systems and allow lifelong learning and adaptation, therefore, supporting the establishment of entirely autonomous cybersecurity ecosystems
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