Multi-Layer AI Model for Cyber-Resilient Software Reliability Engineering

The growing sophistication of contemporary software systems and the emergence of more advanced cyber threatening have generated a dire necessity of a combined solution that is secure and reliable. Conventional methods of software reliability engineering cannot be used to deal with the dynamic and changing cyber threats. The given paper suggests a Multi-Layer AI Model of Cyber-Resilient Software Reliability Engineering, which integrates the methodologies of artificial intelligence with the layered architecture framework to provide better performance and resilience to the systems. The suggested model uses several layers, such as data acquisition, preprocessing, prediction, threat detection, decision-making, and self-healing, to offer the real-time monitoring and adaptive response functionality. Deep learning and machine learning algorithms are used to provide high accuracy in prediction of system failure and in detecting cyber threats. Simplified data is also used in order to generate the system with the help of automated response mechanisms and feedback learning to continue improving the performance Experimental analysis of simulated datasets demonstrates that the proposed model is better than the traditional models such as convolutional neural networks, recurrent neural networks, and Random Forest models in the level of accuracy, precision, recall, F1-score, and response time. The results show that the multi-layer architecture is useful in the process of attaining improved cyber resilience and software reliability. The suggested framework offers a scalable, versatile, and effective approach to the current cybersecurity issues and can be implemented to other areas, such as cloud computing, Internet of Things, and enterprise systems.