Malware Detection and Prevention in Internet of Things Networks Using Deep Learning

다국어 초록 (Multilingual Abstract)

This paper presents a deep learning-based approach for detecting malware in IoT networks, focusing on malicious traffic generated by specific bots and malware. The proposed system addresses key IoT security challenges, including evolving threats and device resource limitations, by leveraging advanced neural network architectures. Our framework aims to enhance detection accuracy and scalability while maintaining data integrity and confidentiality across interconnected IoT environments. To this end, the study explores the integration of hybrid neural models with privacy-preserving mechanisms to ensure efficient and secure deployment in real-world IoT scenarios.