Stego-Vector Driven Simultaneous Covert Channels in Streaming Applications

Network covert channels (NCCs) are an important technique for preserving transmission privacy but are often limited in capacity and reliability. Streaming applications, such as online calls and video conferencing, offer new opportunities to enhance NCCs due to their inherently simultaneous and multi-streamed architecture. To exploit this potential, we introduce a stego-vector model that represents the traffic foundation of streaming applications, where each vector component corresponds to an independent traffic feature stream. Based on this model, we estimate the achievable covert capacity of each covert channel and propose an orchestration algorithm that dynamically schedules channel usage to optimize overall performance. We implement multiple NCC algorithms under diverse streaming traffic scenarios, all coordinated by the proposed stego-vector model and orchestration algorithm. Experimental results show that our method improves transmission capacity by 3–25% while reducing exposure risk by more than 10% compared with classical multi-channel combination approaches, and achieves over 40% higher capacity than single-channel methods. Deployment experiments further demonstrate the lightweight nature of the proposed framework. To the best of our knowledge, this work presents the first systematic design of simultaneous covert channels over real-world streaming traffic, validating both the theoretical soundness and practical effectiveness of the proposed multi-stream covert communication framework.