Spatial Context Tree Weighting for Physical Unclonable Functions

AbstractMost cities in the United States rely on zoning to address important planning-related issues within their jurisdictions. Planners often use GIS tools to analyze these issues in a spatial context. ESRI’s ArcGIS Urban software seeks to provide the planning profession with a GIS-based solution for various challenges, including zoning’s impacts on the built environment and housing capacity.This research explores the use of ArcGIS Urban for assessing the existing zoning and comprehensive plans in meeting the projected residential growth in the near future using the City of Arlington, Texas as a case study. The exploration provides examples and lessons for how ArcGIS Urban might be used by planners to accomplish their tasks and highlights the capabilities and limitations of ArcGIS Urban in its current stand. The paper is concluded with some suggestions for future studies.

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Remote Quantum-Safe Authentication of Entities with Physical Unclonable Functions

Photonics ◽

10.3390/photonics8070289 ◽

2021 ◽

Vol 8 (7) ◽

pp. 289

Author(s):

Georgios M. Nikolopoulos

Keyword(s):

Authentication Protocol ◽

Authentication Protocols ◽

Photonic Networks ◽

Physical Unclonable Functions ◽

Quantum Protocols ◽

Open Question ◽

Standard Techniques

Physical unclonable functions have been shown to be a useful resource of randomness for implementing various cryptographic tasks including entity authentication. All the related entity authentication protocols that have been discussed in the literature so far, either they are vulnerable to an emulation attack, or they are limited to short distances. Hence, quantum-safe remote entity authentication over large distances remains an open question. In the first part of this work, we discuss the requirements that an entity authentication protocol has to offer, to be useful for remote entity authentication in practice. Subsequently, we propose a protocol, which can operate over large distances, and offers security against both classical and quantum adversaries. The proposed protocol relies on standard techniques, it is fully compatible with the infrastructure of existing and future photonic networks, and it can operate in parallel with other quantum protocols, including QKD protocols.

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Deep PUF: A Highly Reliable DRAM PUF-Based Authentication for IoT Networks Using Deep Convolutional Neural Networks

Sensors ◽

10.3390/s21062009 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2009

Author(s):

Fatemeh Najafi ◽

Masoud Kaveh ◽

Diego Martín ◽

Mohammad Reza Mosavi

Keyword(s):

Error Correction ◽

Random Access ◽

Error Correction Codes ◽

Access Memory ◽

Deep Convolutional Neural Networks ◽

Device Identification ◽

Physical Unclonable Functions ◽

Server Side ◽

Correction Technique ◽

Reduction Methods

Traditional authentication techniques, such as cryptographic solutions, are vulnerable to various attacks occurring on session keys and data. Physical unclonable functions (PUFs) such as dynamic random access memory (DRAM)-based PUFs are introduced as promising security blocks to enable cryptography and authentication services. However, PUFs are often sensitive to internal and external noises, which cause reliability issues. The requirement of additional robustness and reliability leads to the involvement of error-reduction methods such as error correction codes (ECCs) and pre-selection schemes that cause considerable extra overheads. In this paper, we propose deep PUF: a deep convolutional neural network (CNN)-based scheme using the latency-based DRAM PUFs without the need for any additional error correction technique. The proposed framework provides a higher number of challenge-response pairs (CRPs) by eliminating the pre-selection and filtering mechanisms. The entire complexity of device identification is moved to the server side that enables the authentication of resource-constrained nodes. The experimental results from a 1Gb DDR3 show that the responses under varying conditions can be classified with at least a 94.9% accuracy rate by using CNN. After applying the proposed authentication steps to the classification results, we show that the probability of identification error can be drastically reduced, which leads to a highly reliable authentication.

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