Abstract
The surge in Internet of Things (IoT) device usage has concurrently seen a rise in cyber threats aimed at these devices. Conventional centralized machine learning methods for identifying malware frequently encounter privacy dilemmas and scalability challenges. In this study, we introduce a method based on federated learning to detect IoT malware. This method promotes cooperative learning across dispersed IoT devices, ensuring data confidentiality. We have crafted a federated learning model proficient in identifying a wide array of IoT malware with impressive precision. We put this model to the test using an authentic IoT malware dataset, showcasing its capability in pinpointing malicious operations. Our testing indicates that the federated learning method we suggest surpasses conventional centralized techniques in both accuracy and privacy maintenance. This newly proposed technique holds promise in bolstering IoT security and reducing vulnerabilities linked to IoT malware.
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Dang, QV., Pham, TH. (2024). Detecting IoT Malware Using Federated Learning. In: Nanda, S.J., Yadav, R.P., Gandomi, A.H., Saraswat, M. (eds) Data Science and Applications. ICDSA 2023. Lecture Notes in Networks and Systems, vol 818. Springer, Singapore. https://doi.org/10.1007/978-981-99-7862-5_6
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DOI: https://doi.org/10.1007/978-981-99-7862-5_6
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