Virtual Guard Against DDoS Attack for IoT Network Using Supervised Learning Method

End users can manage, send, and save data remotely over the Internet of Things (IoT) by utilising the internet's infrastructure. However, cyberattacks can be directed at IoT-enabled machines. The hackers will flood the server with numerous requests to temporarily disable the resources or to steal various types of sensitive data. These types of attacks are widespread now, and we aren't taking any precautions to reduce such data thefts. In our system, we train the model with a huge amount of data sets using the linear regression approach, which results in an average accuracy rate of 98 percent. The accuracy varies depending on how many datasets are entered. We can further limit DDoS attacks and related threats using the proposed solution.