The rapid proliferation of mobile applications, IoT-enabled systems, and complex multi-vendor infrastructures has intensified the challenges of ensuring system security, stability, and operational integrity. Traditional manual security assessments are increasingly inadequate to address the volume, diversity, and dynamic nature of contemporary software and hardware ecosystems. This study examines the integration of automated vulnerability assessment and penetration testing (VAPT) with advanced machine learning models, threat intelligence, and fuzzy logic-based control strategies to enhance the detection and mitigation of security risks while ensuring system stability. The research synthesizes methodologies from static and dynamic code analysis for Android applications, automated penetration testing in multi-vendor systems, and stability analysis of power electronic converters using state-space techniques. Additionally, the work explores the application of Internet of Things (IoT) frameworks in monitoring critical infrastructure, agricultural systems, and mobile devices, highlighting the importance of real-time threat intelligence and adaptive detection mechanisms. Key contributions include a detailed evaluation of AI-enhanced penetration testing frameworks, a theoretical model for fuzzy logic-based control in series-parallel resonant converters, and a comprehensive discussion on integrating continuous security testing into DevSecOps pipelines. The findings suggest that combining automated VAPT, predictive machine learning models, and advanced control theory can significantly improve detection accuracy, reduce false positives, and enhance overall system resilience. The study provides a multidisciplinary perspective, emphasizing both cybersecurity and system stability considerations, offering practical guidance for researchers and practitioners in deploying robust and intelligent monitoring frameworks.

Automated Penetration Testing, Machine Learning, Android Security, IoT Monitoring

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