A Machine Learning Approach for Detecting DDOS Attack in IoT Network Using Random Forest Classifier
Keywords:
Machine Learning, Internet of Things (IoT), Classification, Security, AccuracyAbstract
The Internet of Things (IoT) has seen noteworthy growth and advancement in the last ten years, offering innovative keys to address social and industrial challenges. However, ensuring the safety of IoT devices has become a crucial concern because of their vulnerability to cyberattacks, which poses serious hazards and result in serious harm. Although researchers have made strides in this direction, Multiview feature integration and extensive semantic relationship capturing still need to be completed beyond the purview of existing work. Therefore, these methods may be more secure and better at identifying various threats in actual time. This study proposes a new technique using the Random Forest classifier to overcome these obstacles. By harnessing the potential of ensemble learning, this approach combines numerous decision trees to provide accurate and speedy predictions for the rapid and precise identification of threats in IoT networks. UDP-FLOOD, Smurf, HTTP-FLOOD, and SIDDOS are only some network assaults included in the collection. When applied to network traffic data, the Random Forest classifier is a powerful addition to more traditional machine learning-based categorization methods. The proposed Random Forest classifier enhances intrusion detection efficacy and shortens training time, indicating an enhanced solution for improving the network security in IoTs.
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