KNOWLEDGE-AWARE IOT MAINTENANCE PREDICTION USING DEEP LEARNING ON STACK OVERFLOW SECURITY DATA
Dr Pulime Satyanarayana, Chepuri Bharath Kumar, Pagidala Bharath Kumar, Godugu Elishan · Journal of Science Engineering Technology and Management Sciences · 2025
The rapid expansion of the Internet of Things (IoT) has transformed numerous industries by enabling real-time data exchange and automated operations.However, this growth brings significant challenges in maintaining robust security and efficient system performance.Recent projections indicate that the number of IoT devices will surpass 75 billion by 2025, resulting in a massive, interconnected network vulnerable to security threats and maintenance difficulties.Traditional security mechanisms, which rely on manual monitoring and fixed threshold-based detection, are increasingly inadequate in dynamic IoT environments.These conventional approaches often fail to detect new and evolving threats in real time and struggle with predictive maintenance, leading to higher downtime and degraded system performance.Additionally, the heterogeneous nature of IoT devices and the massive data they generate make it difficult for legacy systems to respond quickly and effectively.These limitations highlight the urgent need for more advanced solutions.This research explores the application of deep learning (DL) models to meet these challenges by focusing on security threat detection, performance forecasting, and predictive maintenance in IoT ecosystems.DL techniques offer the ability to process large-scale data, identify anomalies indicative of security issues, and forecast maintenance requirements before failures occur.The strength of this approach lies in its capacity to enhance IoT system reliability and security, reduce downtime risk, and improve performance management.Ultimately, this research aims to enable a shift from reactive to proactive management of IoT systems, supporting their scalability and complexity while ensuring secure and efficient operation for critical applications.