Identification of Proactive Cyber Threats in Cloud-Managed Internet of Things Systems Utilizing Machine Learning Techniques

IoT gadgets and cloud computing have revolutionized how we utilize technology. Increasing connectivity and dependence on cloud-managed IoT technology has raised cyber threat attack surfaces. Reactive security methods often fail against increasingly complex cyber threats. Thus, proactive threat identification systems must detect cyber dangers before they occur. ML is used to provide a new paradigm for proactive cyber threat assessment in cloud-managed IoT systems. Data from telemetry devices, user behaviors, and network traffic is analyzed using ML algorithms to identify cyberattack tendencies. The approach proactively identifies IoT security issues to reduce data breaches and service outages. First, the paper examines IoT cyber threats and insufficient security protocols. It then examines how ML methods including supervised and unsupervised learning, anomaly detection, predictive modeling, and threat identification are used. The study evaluates decision trees, support vector machines, neural networks, and ensemble techniques for proactive cyber threat detection. The research uses real-world and simulated IoT data to test the methods. The results show that ML can detect malware infections, unlawful access attempts, and DDoS attacks. The framework's performance is measured by detection accuracy, false positive rate, and reaction time to balance security and usability. In addition, the research covers using cloud-managed IoT security protocols with ML-based threat identification. It addresses data privacy, generalizing models, and ML algorithm scalability in big IoT rollouts. ML techniques can transform cloud-based IoT security, the study concludes. It emphasizes ML models that learn and adapt to stay up with new cyber threats. Further research should investigate advanced ML methods like deep learning and reinforcement learning and create AI models that are easy to comprehend and utilize for open threat identification. ML-based proactive threat identification is proposed in this paper, which adds to Internet of Things security literature. The process it outlines to make cloud-managed IoT systems more cyber-resistant ensures the security and reliability of all linked devices in the digital ecosystem.