The Impact of Feature Importance on Spoofing Attack Detection in IoT Environment
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Abstract
The Internet of Things (IoT) is an expanding domain that can revolutionize different industries. Nevertheless, security is among the multiple challenges that it encounters. A major threat in the IoT environment is spoofing attacks, a type of cyber threat in which malicious actors masquerade as legitimate entities.
This research aims to develop an effective technique for detecting spoofing attacks for IoT security by utilizing feature-importance methods. The suggested methodology involves three stages: preprocessing, selection of important features, and classification. The feature importance determines the most significant characteristics that play a role in detecting spoofing attacks. This is achieved via two techniques: decision tree (DT) and mutual information (MI). For classification, adaptive boosting (AdaBoost), XGBoost and categorical boosting (CatBoosting) are used to categorize incoming data as normal or spoofing. The experimental results indicate the efficiency of the suggested approach for correctly identifying spoofing attacks with high accuracy, fewer false positives, and reduced time needed. By utilizing feature importance and robust classification algorithms, the system can accurately differentiate between legitimate and malicious IoT traffic, thereby improving the overall security of IoT networks. The CatBoost classifier outperformed the AdaBoost and XGBoost classifiers in terms of accuracy.
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