MCRO-PUF:A Novel Modified Crossover RO-PUF with an Ultra-Expanded CRP Space

With the expanding use of the Internet of Things(IoT)devices and the connection of humans and devices to the Internet,the need to provide security in this field is constantly growing.The conventional cryptographic solutions need the IoT device to store secret keys in its non-volatile memory(NVM)leading the system to be vulnerable to physical attacks.In addition,they are not appropriate for IoT applications due to their complex calculations.Thus,physically unclonable functions(PUFs)have been introduced to simultaneously address these issues.PUFs are lightweight and easy-toaccess hardware security primitives which employ the unique characteristics of integrated circuits(ICs)to generate secret keys.Among all proposed PUFs,ring oscillator PUF(RO-PUF)has had amore suitable structure for hardware implementation because of its high reliability and easier providing of circuital symmetry.However,RO-PUF has not been so attractive for authentication purposes due to its limited supported challenge-response pairs(CRPs).A few efforts have been made in recent years that could successfully improve the RO-PUF CRP space,such as configurable RO-PUF(CRO-PUF).In this paper,by considerably improving the CRO-PUF structure and adding spare paths,we propose a novel strong RO-PUF structure that exponentially grows the CRP space and dramatically reduces the hardware cost.We implement our design on a simple and low-cost FPGA chip named XC6SLX9-2tqg144,stating that the proposed design can be used in IoT applications.In addition,to improve the CRP space,our design creates a suitable improvement in different security/performance terms of the generated responses,and dramatically outperforms the state-of-the-art.The average reliability,uniqueness,and uniformity of the responses generated are 99.55%,48.49%,and 50.99%,respectively.

Recurrent Polynomial Neural Networks for Enhancing Performance of GPS in Electric Systems

Global Positioning System (GPS) is a worldwide satellite system that provides navigation, positioning, and timing for both military and civilian applications. GPS based time reference provides inexpensive but highly-accurate timing and synchronization capability and meets requirements in power system fault location, monitoring, and control. In the present era of restructuring and modernization of electric power utilities, the applications of GIS/GPS technology in power industry are growing and covering several technical and man-agement activities. Because of GPS receiver’s error sources are time variant, it is necessary to remove the GPS measurement noise. This paper presents novel recurrent neural networks called the Recurrent Pi-Sigma Neural Network (RPSNN) and Recurrent Sigma-Pi Neural Network (RSPNN). The proposed NNs have been used as predictor in GPS receivers timing errors. The NNs were trained using the dynamic Back Propagation (BP) algorithm. The actual data collection was used to test the performance of the proposed NNs. The ex-perimental results obtained from a Coarse Acquisition (C/A)-code single-frequency GPS receiver strongly support the potential of the method using RPSNN to give high accurate timing. The GPS timing RMS error reduces from 200 to less than 40 nanoseconds.