Qiang Li

Liu Yang, Qiang Li, Xiaoyang Ren et al.

Radio Frequency Fingerprint Identification (RFFI), which exploits non-ideal hardware-induced unique distortion resident in the transmit signals to identify an emitter, is emerging as a means to enhance the security of communication systems. Recently, machine learning has achieved great success in developing state-of-the-art RFFI models. However, few works consider cross-receiver RFFI problems, where the RFFI model is trained and deployed on different receivers. Due to altered receiver characteristics, direct deployment of RFFI model on a new receiver leads to significant performance degradation. To address this issue, we formulate the cross-receiver RFFI as a model adaptation problem, which adapts the trained model to unlabeled signals from a new receiver. We first develop a theoretical generalization error bound for the adaptation model. Motivated by the bound, we propose a novel method to solve the cross-receiver RFFI problem, which includes domain alignment and adaptive pseudo-labeling. The former aims at finding a feature space where both domains exhibit similar distributions, effectively reducing the domain discrepancy. Meanwhile, the latter employs a dynamic pseudo-labeling scheme to implicitly transfer the label information from the labeled receiver to the new receiver. Experimental results indicate that the proposed method can effectively mitigate the receiver impact and improve the cross-receiver RFFI performance.

Mingfeng Xin, Hui Wen, Liting Deng et al.

The rapid growth of the Industrial Internet of Things (IIoT) has brought embedded systems into focus as major targets for both security analysts and malicious adversaries. Due to the non-standard hardware and diverse software, embedded devices present unique challenges to security analysts for the accurate analysis of firmware binaries. The diversity in hardware components and tight coupling between firmware and hardware makes it hard to perform dynamic analysis, which must have the ability to execute firmware code in virtualized environments. However, emulating the large expanse of hardware peripherals makes analysts have to frequently modify the emulator for executing various firmware code in different virtualized environments, greatly limiting the ability of security analysis. In this work, we explore the problem of firmware re-hosting related to the real-time operating system (RTOS). Specifically, developers create a Board Support Package (BSP) and develop device drivers to make that RTOS run on their platform. By providing high-level replacements for BSP routines and device drivers, we can make the minimal modification of the firmware that is to be migrated from its original hardware environment into a virtualized one. We show that an approach capable of offering the ability to execute firmware at scale through patching firmware in an automated manner without modifying the existing emulators. Our approach, called static binary-level porting, first identifies the BSP and device drivers in target firmware, then patches the firmware with pre-built BSP routines and drivers that can be adapted to the existing emulators. Finally, we demonstrate the practicality of the proposed method on multiple hardware platforms and firmware samples for security analysis. The result shows that the approach is flexible enough to emulate firmware for vulnerability assessment and exploits development.

Quanyu Dai, Qiang Li, Jian Tang et al.

Learning low-dimensional representations of networks has proved effective in a variety of tasks such as node classification, link prediction and network visualization. Existing methods can effectively encode different structural properties into the representations, such as neighborhood connectivity patterns, global structural role similarities and other high-order proximities. However, except for objectives to capture network structural properties, most of them suffer from lack of additional constraints for enhancing the robustness of representations. In this paper, we aim to exploit the strengths of generative adversarial networks in capturing latent features, and investigate its contribution in learning stable and robust graph representations. Specifically, we propose an Adversarial Network Embedding (ANE) framework, which leverages the adversarial learning principle to regularize the representation learning. It consists of two components, i.e., a structure preserving component and an adversarial learning component. The former component aims to capture network structural properties, while the latter contributes to learning robust representations by matching the posterior distribution of the latent representations to given priors. As shown by the empirical results, our method is competitive with or superior to state-of-the-art approaches on benchmark network embedding tasks.