DeepSpoof: Deep Reinforcement Learning-Based Spoofing Attack in Cross-Technology Multimedia Communication
Demin Gao, Liyuan Ou, Ye Liu, Qing Yang, Honggang Wang
Abstract
Cross-technology communication is essential for the Internet of Multimedia Things (IoMT) applications, enabling seamless integration of diverse media formats, optimized data transmission, and improved user experiences across devices and platforms. This integration drives innovative and efficient IoMT solutions in areas like smart homes, smart cities, and healthcare monitoring. However, this integration of diverse wireless standards within cross-technology multimedia communication increases the susceptibility of wireless networks to attacks. Current methods lack robust authentication mechanisms, leaving them vulnerable to spoofing attacks. To mitigate this concern, we introduce DeepSpoof, a spoofing system that utilizes deep learning to analyze historical wireless traffic and anticipate future patterns in the IoMT context. This innovative approach significantly boosts an attacker's impersonation capabilities and offers a higher degree of covertness compared to traditional spoofing methods. Rigorous evaluations, leveraging both simulated and real-world data, confirm that DeepSpoof significantly elevates the average success rate of attacks.