AI-Empowered Malware Detection System for Iot
Abstract
The Internet of Things (IoT) has revolutionized the way we live and work. However, the increased connectivity of IoT devices has also made them a target for malware attacks. Traditional malware detection methods are not always effective against IoT malware, as they often rely on signatures that attackers can easily circumvent. In this paper, we propose an AI-powered malware detection system for IoT. Our system uses a hybrid deep learning approach that combines convolutional neural networks (CNN) and long short-term memory networks (LSTM). CNNs are used to extract features from IoT malware binary code, and LSTM networks are used to model the temporal relationships between these features. We evaluate the system against his three datasets of publicly available IoT malware. As a result, we found that our system achieved him a high accuracy of 99.98% in detecting his IoT malware. We also show that our system is effective against zero-day IoT malware, malware that has not yet been discovered by security researchers.
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