論文

The rapid diffusion of mobile communication technology and smart phone devices has raised the concerns about the security problems of mobile phone accessories. Despite a few effort has been made in this aspect, there are still some deficiencies such as poor compatibility and limited application scope. This article presents a trusted mobile phone accessory identification scheme. Our method uses physically unclonable function (PUF) as hardware security primitives which are embedded in mobile phone accessories to generate digital signatures, so that they can be applied to identify authorized mobile phone accessories. We finally implement the proposed scheme on a field programmable gate array (FPGA) platform. Experimental results show that our solution can effectively identify trusted mobile phone accessories.

Traffic flows with different requirements of quality of service (QoS requirements) are aggregated into different QoS classes to provide differentiated services (Diffserv) and better quality of experience (QoE) for users. The existing aggregation approaches/QoS mapping methods are based on quantitative QoS requirements and static QoS classes. However, they are typically qualitative and time-varying at the edge of the beyond fifth generation (B5G) networks. Therefore, the artificial intelligence technology of preference logic is applied in this paper to achieve an intelligent method for edge computing, called the preference logic based aggregation model (PLM), which effectively groups flows with qualitative requirements into dynamic classes. First, PLM uses preferences to describe QoS requirements of flows, and thus can deal with both quantitative and qualitative cases. Next, the potential conflicts in these preferences are eliminated. According to the preferences, traffic flows are finally mapped into dynamic QoS classes by logic reasoning. The experimental results show that PLM presents better performance in terms of QoE satisfaction compared with the existing aggregation methods. Utilizing preference logic to group flows, PLM implements a novel way of edge intelligence to deal with dynamic classes and improves the Diffserv for massive B5G traffic with quantitative and qualitative requirements.

With the advancement of the Internet of Things (IoTs) technology, security issues have received an increasing amount of attention. Since IoT devices are typically resource-limited, con-ventional security solutions, such as classical cryptography, are no longer applicable. A physically unclonable function (PUF) is a hardware-based, low-cost alternative solution to provide security for IoT devices. It utilizes the inherent nature of hardware to generate a random and unpredictable fingerprint to uniquely identify an IoT device. However, despite existing PUFs having exhibited a good performance, they are not suitable for effective application on resource-constrained IoT devices due to the limited number of challenge-response pairs (CRPs) generated per unit area and the large hardware resources overhead. To solve these problems, this article presents an ultra-lightweight reconfigurable PUF solution, which is named RPPUF. Our method is built on pico-PUF (PPUF). By incorporating configurable logics, one single RPPUF can be instantiated into multiple samples through configurable information K. We implement and verify our design on the Xilinx Spartan-6 field programmable gate array (FPGA) microboards. The experimental results demonstrate that, compared to previous work, our method increases the uniqueness, reliability and uniformity by up to 4.13%, 16.98% and 10.5%, respectively, while dramatically reducing the hardware resource overhead by 98.16% when a 128-bit PUF response is generated. Moreover, the bit per cost (BPC) metric of our proposed RPPUF increased by up to 28.5 and 53.37 times than that of PPUF and the improved butterfly PUF, respectively. This confirms that the proposed RPPUF is ultra-lightweight with a good performance, making it more appropriate and efficient to apply in FPGA-based IoT devices with constrained resources.

Emerging physical layer identification methods have demonstrated the capability to complement and enhance the device authentication of Internet of Things networks by exploiting the uncontrollable, unclonable, and unforgeable radiometric features resulted from randomly generated hardware imperfection in wireless devices. Multiple feature-based identification has proven an efficient and feasible approach to improving identification performance. At the same time, the lack of radiometric features effective for device identification is a major problem. Most of the existing features are derived from the view of the time, frequency, or phase domain. In this study, we explore the graph domain of wireless frame's preambles and propose a new radiometric feature called normalized horizontal visibility graph Shannon entropy (HVGE). At first, we introduce a preprocessing consisting of sample truncation and downsampling to enable the adjustment between the computational time of visibility graph (VG) conversion and the identification performance. Secondly, we propose the calculation method of the new HVGE feature from the VG representation. Finally, an experimental study using 50 off-the-shelf wireless devices was conducted to investigate the impact of the preprocessing parameters and the effect of noise and feature combinations on the identification performance gain.