DETECTION AND MITIGATION OF CYBER THREATS IN IOT-BASED EMBEDDED SYSTEMS USING MACHINE LEARNING MODELS IN DATA FORENSICS

The increasing integration of Internet of Things (IoT) embedded systems in critical sectors like healthcare has heightened concerns over cybersecurity threats.These systems, due to their interconnected and often resourceconstrained nature, are vulnerable to attacks that can compromise sensitive data and disrupt vital operations.This article examines the use of ML models to detect and mitigate cyber threats within IoT-based embedded systems, with a particular focus on healthcare environments.ML models offer robust solutions for data forensics by identifying anomalies in network behaviour, distinguishing between normal and malicious activities, and enhancing the accuracy of threat detection.Key techniques include training datasets on network patterns associated with both regular and suspicious activities, enabling these models to learn and recognize cyber threats effectively.The study delves into various ML methods, such as anomaly detection and supervised classification, to understand their application in IoT threat mitigation.Additionally, it explores the unique challenges faced in securing IoT systems, such as limited computational power, and the importance of secure software implementations to prevent vulnerabilities.Through case studies and experimental evaluations, the article highlights the effectiveness of ML models in improving the reliability and security of IoT-based embedded systems.The discussion extends to future implications for cybersecurity in IoT, emphasizing the potential for advanced, adaptive models to ensure proactive threat mitigation and data protection in sensitive applications.