Cyber Attack Detection in an Internet of Things Employing Random Forest

The Internet of Things is a vast system of interconnected devices. These gadgets are becoming increasingly commonplace in vital applications.  As a result, cybercriminals are directing more of their attention toward the IoT. To protect the IoT from intrusion, we employ Random Forest, a popular machine-learning algorithm for classification tasks, in this work. To implement the proposed method, labeled data depicting both normal IoT device operation and malicious attacks must be collected.  Our approach is 95% accurate. Because the advantages of using random forests can be used with smaller datasets and normal computational resources, we believe our method is a good one for detecting and avoiding IoT attacks. This paper introduces a method for detecting cyber-attacks in IoT environments. It uses the Random Forest (RF) algorithm, which is well-known for its accuracy and resilience in classification tasks. To better protect the Internet of Things (IoT) from ever-changing cyber threats, this method offers a dependable and scalable solution.