With the popularization of new technologies, telephone fraud has become the main means of stealing money and personal identity information. Taking inspiration from the website authentication mechanism, we propose an end-to-end data modem scheme that transmits the caller’s digital certificates through a voice channel for the recipient to verify the caller’s identity. Encoding useful information through voice channels is very difficult without the assistance of telecommunications providers. For example, speech activity detection may quickly classify encoded signals as non-speech signals and reject input waveforms. To address this issue, we propose a novel modulation method based on linear frequency modulation that encodes 3 bits per symbol by varying its frequency, shape, and phase, alongside a lightweight MobileNetV3-Small-based demodulator for efficient and accurate signal decoding on resource-constrained devices. This method leverages the unique characteristics of linear frequency modulation signals, making them more easily transmitted and decoded in speech channels. To ensure reliable data delivery over unstable voice links, we further introduce a robust framing scheme with delimiter-based synchronization, a sample-level position remedying algorithm, and a feedback-driven retransmission mechanism. We have validated the feasibility and performance of our system through expanded real-world evaluations, demonstrating that it outperforms existing advanced methods in terms of robustness and data transfer rate. This technology establishes the foundational infrastructure for reliable certificate delivery over voice channels, which is crucial for achieving strong caller authentication and preventing telephone fraud at its root cause.

Fuzz testing is a widely adopted technique for uncovering bugs and security vulnerabilities in embedded firmware. However, many embedded systems heavily rely on peripherals, rendering conventional fuzzing techniques ineffective. When peripheral responses are missing or incorrect, fuzzing a firmware may crash or exit prematurely, significantly limiting code coverage. While prior re-hosting approaches have made progress in simulating Memory-Mapped Input/Output (MMIO) and interrupt-based peripherals, they either ignore Direct Memory Access (DMA) or handle it oversimplified. In this work, we present ADFEmu, a novel automated firmware re-hosting framework that enables effective fuzzing of DMA-enabled firmware. ADFEmu integrates concolic execution with large language models (LLMs) to semantically emulate DMA operations and synthesize peripheral input sequences intelligently. Specifically, it learns DMA transfer patterns from the firmware’s context and employs guided symbolic execution to explore deeper and more diverse execution paths. This approach allows firmware to operate stably without hardware dependencies while achieving higher fidelity in emulation. Evaluated on real-world embedded firmware samples, ADFEmu achieves a 100% re-hosting success rate, improves total execution path exploration by 5.31%, and triggers more crashes compared to the state-of-the-art. These results highlight ADFEmu’s effectiveness in overcoming long-standing limitations of DMA emulation and its potential to advance automated vulnerability discovery in peripheral-rich embedded environments.