Machine Learning Based Two-Tier Security Mechanism for IoT Devices Against DDoS Attacks

IoT devices are becoming an increasingly important part of our everyday lives, and their worth is rising with each passing year. Because IoT devices capture and handle all of our personal and private data, they are a primary target for cyber attackers. Due to the limited processing power and memory capacity of IoT devices, it is challenging to apply complicated security algorithms. The development of a lightweight security mechanism for IoT devices is necessary. In this context, we create a two-tier security solution for Internet of Things devices that protects against DDoS attacks, the most well-known kind of cyber assault. The suggested solution makes extensive use of statistical technologies and machine learning techniques at several tiers to effectively recognise DDoS attacks. The suggested technique made advantage of active learning to determine the appropriate attributes for detecting DDoS attack traffic.