Proactive Cybersecurity Frameworks using Machine Learning for Anomaly Detection in Wireless IoT Networks

A Novel Proactive Cybersecurity Framework for Router-based Anomaly Detection in Wireless IoT Networks using Machine Learning Techniques It discusses the model complexity and energy-drain trade-offs, posing challenges to find a system that balances detection accuracy with minimal resource usage. The framework shows the adaptability of dynamic network conditions and resilience to adversarial attacks through extensive simulations. Federated learning also improves scalability and communication security as the number of IoT devices increases. Its analysis emphasizes devising strategic balance provisions between performance and efficiency to build scalable and secure IoT systems in constraint measure scenarios. This research is helpful in ushering smart, adaption, efficient and smarter way forward towards future IoT deployment.