Leveraging oversampling techniques in machine learning models for multi-class malware detection in smart home applications

Smarthome applications are becoming increasingly popular due to their ability to provide safety, comfort, and remote assistance. These applications are usually controlled using a smart home controller, which is often the target of malware attacks. A successful attack may result in financial loss, disclosure of personal and/or sensitive information, or even loss of human lives. Although machine learning models have been used in existing research for detecting multi-class malware attacks in smart home systems, they did not explicitly address the class imbalance problem in such cases. In addition, the use of ensemble learner is expected to provide improved performance. To address this, we investigated different oversampling techniques to increase the number of samples in the minority classes and incorporated ensemble learners to see their impact on the prediction performance. Experimental evaluation shows significant improvements (4-5%) in terms of accuracy, precision, recall, and F-1 score. Index Terms—Oversampling Techniques, Ensemble Models, Multi-class Malware Detection

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.