<ul data-eligibleForWebStory="true"><li>IoTGeM is a new approach for behavior-based attack detection focusing on generalizability and improved performance in IoT networks.</li><li>It introduces an enhanced rolling window method for feature extraction and utilizes a multi-step feature selection process with a Genetic Algorithm guided by external feedback.</li><li>To avoid overfitting, models are trained and tested using separate datasets and rigorously evaluated with various machine learning algorithms and datasets.</li><li>The IoTGeM models outperform traditional flow-based models in generalization, achieving high F1 scores for various attack types on unseen data.</li><li>The approach also utilizes the SHAP explainable AI technique to identify the key features contributing to accurate attack detection.</li></ul>

IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection

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