Intelligent Automated Testing Frameworks for IoT Networks Utilizing Machine Learning for Advanced Anomaly Detection
Marian Ileana, Maria Miroiu · 2024
The widespread deployment of Internet of Things (IoT) networks has brought new challenges in terms of ensuring system reliability and security. This paper presents intelligent automated testing frameworks designed specifically for IoT environments, leveraging machine learning for advanced anomaly detection. Traditional testing methods are often insufficient in the dynamic and heterogeneous landscape of IoT systems. The approach explores the use of machine learning algorithms to identify patterns and anomalies in real-time, thereby increasing the robustness and resilience of IoT networks. By integrating predictive analytics and real-time monitoring, the proposed framework not only detects failures but also anticipates potential problems, thus facilitating system maintenance. This innovative approach underscores the importance of intelligent automation in addressing the complexities of modern IoT ecosystems.