Optimized Decision Trees to Detect IoT Malware

The proliferation of the Internet of Things (IoT) devices has led to an increased risk of cyberattacks and malicious activities, including the spread of malware. To mitigate these risks, it is crucial to develop effective approaches for detecting IoT malware. In this study, we propose a framework for detecting IoT malware using optimized decision trees with AdaBoost. We use two widely used datasets, NSL-KDD and CICIDS2017, to evaluate the performance of the proposed framework. The framework includes feature selection and hyperparameter tuning to enhance the performance of the model. Our results show that the proposed framework achieves high accuracy, precision, recall, F1 score, and AUC-ROC in detecting malware attacks. However, the study also has limitations, such as the focus on network-level features and the limited evaluation on specific datasets. Future research can address these limitations by testing the proposed framework on more diverse datasets and exploring different machine learning algorithms and techniques. Overall, our study provides a promising approach to detect IoT malware and can contribute to the development of more robust and effective approaches for network intrusion detection.