A Hybrid Approach to Malware Detection in Mobile Networks
Saliha Menacer, Mohamed Faouzi Zerarka, Abdelhakim Cheriet, Smain Femmam · 2024
The advancement of IoT and 6G networks has heightened privacy and security challenges in mobile networks, including malware, DDoS attacks, and unauthorized access. To address these issues, we propose a hybrid data-driven approach using deep learning and machine learning for detecting malicious activities. Additionally, we integrate blockchain technology to enhance data security, leveraging its decentralization, persistence, anonymity, and traceability.