scholarly journals Intrinsic Run-time Row Hammer PUFs: Leveraging the Row Hammer Effect for Run-Time Cryptography and Improved Security

2018 ◽  
Author(s):  
Nikolaos Athanasios Anagnostopoulos ◽  
Tolga Arul ◽  
Yufan Fan ◽  
Christian Hatzfeld ◽  
André Schaller ◽  
...  
Keyword(s):  
Random Access ◽  
Physical Unclonable Functions ◽  
Generation Times ◽  
Speed Up ◽  
Run Time ◽  
Commercial Off The Shelf ◽  
Cost Efficient ◽  
Statistical Relationships ◽  
The Times ◽  
Time Required

Physical Unclonable Functions (PUFs) based on the retention times of the cells of a Dynamic Random Access Memory (DRAM) can be utilised for the implementation of cost-efficient and lightweight cryptographic protocols. However, as recent work has demonstrated, the times needed in order to generate their responses may prohibit their widespread usage. In order to address this issue, the Row Hammer PUF has been proposed by Schaller et al. [1], which leverages the row hammer effect in DRAM modules to reduce the retention times of their cells and, therefore, significantly speed up the generation times for the responses of PUFs based on these retention times. In this work, we extend the work of Schaller et al. by presenting a run-time accessible implementation of this PUF and further reducing the time required for the generation of its responses. Additionally, we also provide a more thorough investigation of the effects of temperature variations on the the Row Hammer PUF and briefly discuss potential statistical relationships between the cells used to implement it. As our results prove, the Row Hammer PUF could potentially provide an adequate level of security for Commercial Off-The-Shelf (COTS) devices, if its dependency on temperature is mitigated, and, may therefore, be commercially adopted in the near future.

scholarly journals Intrinsic Physical Unclonable Function (PUF) Sensors in Commodity Devices

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2428 ◽  
Author(s):  
Shuai Chen ◽  
Bing Li ◽  
Yuan Cao
Keyword(s):  
Random Access ◽  
Random Access Memory ◽  
Experimental Results ◽  
Raspberry Pi ◽  
Key Generation ◽  
Access Memory ◽  
Physical Unclonable Functions ◽  
Security Authentication ◽  
Commercial Off The Shelf ◽  
Intrinsic Dynamic

The environment-dependent feature of physical unclonable functions (PUFs) is capable of sensing environment changes. This paper presents an analysis and categorization of a variety of PUF sensors. Prior works have demonstrated that PUFs can be used as sensors while providing a security authentication assurance. However, most of the PUF sensors need a dedicated circuit. It can be difficult to implemented in commercial off-the-shelf devices. This paper focuses on the intrinsic Dynamic Random Access Memory (DRAM) PUF-based sensors, which requires no modifications for hardware. The preliminary experimental results on Raspberry Pi have demonstrated the feasibility of our design. Furthermore, we configured the DRAM PUF-based sensor in a DRAM PUF-based key generation scheme which improves the practicability of the design.

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

Sensors ◽  
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.

scholarly journals A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Sumedh Yadav ◽  
Mathis Bode
Keyword(s):  
Prediction Accuracy ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Large Datasets ◽  
Test Results ◽  
Accuracy Test ◽  
Reasonable Proportion ◽  
Speed Up ◽  
Run Time ◽  
Information Graph

Abstract A scalable graphical method is presented for selecting and partitioning datasets for the training phase of a classification task. For the heuristic, a clustering algorithm is required to get its computation cost in a reasonable proportion to the task itself. This step is succeeded by construction of an information graph of the underlying classification patterns using approximate nearest neighbor methods. The presented method consists of two approaches, one for reducing a given training set, and another for partitioning the selected/reduced set. The heuristic targets large datasets, since the primary goal is a significant reduction in training computation run-time without compromising prediction accuracy. Test results show that both approaches significantly speed-up the training task when compared against that of state-of-the-art shrinking heuristics available in LIBSVM. Furthermore, the approaches closely follow or even outperform in prediction accuracy. A network design is also presented for a partitioning based distributed training formulation. Added speed-up in training run-time is observed when compared to that of serial implementation of the approaches.

scholarly journals Ferroelectric HfO2-based materials for next-generation ferroelectric memories

2016 ◽  
Vol 06 (02) ◽  
pp. 1630003 ◽  
Author(s):  
Zhen Fan ◽  
Jingsheng Chen ◽  
John Wang
Keyword(s):  
Random Access ◽  
Complementary Metal Oxide Semiconductor ◽  
Ferroelectric Materials ◽  
Oxide Semiconductor ◽  
Next Generation ◽  
New Class ◽  
Generation Cost ◽  
Potential Applications ◽  
Cost Efficient ◽  
Ferroelectric Memories

Ferroelectric random access memory (FeRAM) based on conventional ferroelectric perovskites, such as Pb(Zr,Ti)O3 and SrBi2Ta2O9, has encountered bottlenecks on memory density and cost, because those conventional perovskites suffer from various issues mainly including poor complementary metal-oxide-semiconductor (CMOS)-compatibility and limited scalability. Next-generation cost-efficient, high-density FeRAM shall therefore rely on a material revolution. Since the discovery of ferroelectricity in Si:HfO2 thin films in 2011, HfO2-based materials have aroused widespread interest in the field of FeRAM, because they are CMOS-compatible and can exhibit robust ferroelectricity even when the film thickness is scaled down to below 10 nm. A review on this new class of ferroelectric materials is therefore of great interest. In this paper, the most appealing topics about ferroelectric HfO2-based materials including origins of ferroelectricity, advantageous material properties, and current and potential applications in FeRAM, are briefly reviewed.

scholarly journals Pitfall of the Strongest Cells in Static Random Access Memory Physical Unclonable Functions

Sensors ◽  
2018 ◽  
Vol 18 (6) ◽  
pp. 1776 ◽  
Author(s):  
Mingyang Gong ◽  
Hailong Liu ◽  
Run Min ◽  
Zhenglin Liu
Keyword(s):  
Random Access ◽  
Random Access Memory ◽  
Static Random Access Memory ◽  
Access Memory ◽  
Physical Unclonable Functions

The Artisential® Articulated Laparoscopic Forceps: A Dry Lab Study to Examine Dexterity and Learning Effects in Operators with Different Levels of Laparoscopic Experience

2021 ◽  
Author(s):  
Ibrahim Darwich ◽  
Mohammad Abuassi ◽  
Christel Weiss ◽  
Dietmar Stephan ◽  
Frank Willeke
Keyword(s):  
Learning Effect ◽  
Training Session ◽  
Transfer Task ◽  
Learning Effects ◽  
Laparoscopic Instrument ◽  
Significant Difference ◽  
The Times ◽  
Time Required ◽  
Laparoscopic Experience ◽  
Different Levels

Purpose: The advent of robotic surgery has highlighted the advantages of articulation. This dry-lab study examined the dexterity and learning effect of a new articulated laparoscopic instrument: the ArtiSential® forceps (LIVSMED, Seongnam, Republic of Korea). Methods: A peg board task was designed. Three groups of volunteers with varying levels of laparoscopic expertise were organized to perform the task: expert, intermediate and novice. The participants performed the task using articulated and straight instruments, once before a 30-min training session and once afterwards. The times required to perform the task were recorded. The performances were analyzed and compared between the groups as well as between the straight and articulated instruments. Results: The experts were significantly faster than the novices with both instruments before the 30-min training session (p = 0.0317 for each instrument). No significant time difference was found among the three groups after the 30-min training session. The decrease in the time required to perform the peg-transfer task with the articulated instrument was significantly greater in the novice and intermediate groups (p = 0.0159 for each group). No significant difference in time reduction was observed between the groups with the straight instrument. Regardless of the user, the articulated device was associated with faster task performance than the straight device after 8 hours of training (p = 0.0039). Conclusion: The ArtiSential® articulated device can improve dexterity. A significantly greater learning effect was observed in the novice and intermediate groups in comparison with experts. A plateau in the learning curve was observed after a few hours of training.

scholarly journals AC_ICAP: A Flexible High Speed ICAP Controller

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Luis Andres Cardona ◽  
Carles Ferrer
Keyword(s):  
High Speed ◽  
Access Port ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Speed Up ◽  
Run Time ◽  
Ip Cores ◽  
Similar Solutions ◽  
Internal Configuration ◽  
Run Time Reconfiguration

The Internal Configuration Access Port (ICAP) is the core component of any dynamic partial reconfigurable system implemented in Xilinx SRAM-based Field Programmable Gate Arrays (FPGAs). We developed a new high speed ICAP controller, named AC_ICAP, completely implemented in hardware. In addition to similar solutions to accelerate the management of partial bitstreams and frames, AC_ICAP also supports run-time reconfiguration of LUTs without requiring precomputed partial bitstreams. This last characteristic was possible by performing reverse engineering on the bitstream. Besides, we adapted this hardware-based solution to provide IP cores accessible from the MicroBlaze processor. To this end, the controller was extended and three versions were implemented to evaluate its performance when connected to Peripheral Local Bus (PLB), Fast Simplex Link (FSL), and AXI interfaces of the processor. In consequence, the controller can exploit the flexibility that the processor offers but taking advantage of the hardware speed-up. It was implemented in both Virtex-5 and Kintex7 FPGAs. Results of reconfiguration time showed that run-time reconfiguration of single LUTs in Virtex-5 devices was performed in less than 5 μs which implies a speed-up of more than 380x compared to the Xilinx XPS_HWICAP controller.

scholarly journals On the Use of Magnetic RAMs in Field-Programmable Gate Arrays

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
Y. Guillemenet ◽  
L. Torres ◽  
G. Sassatelli ◽  
N. Bruchon
Keyword(s):  
Power Consumption ◽  
Random Access ◽  
Switching Scheme ◽  
Fpga Design ◽  
Gate Arrays ◽  
Magnetic Switching ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Time Required ◽  
Magnetic Tunneling

This paper describes the integration of field-induced magnetic switching (FIMS) and thermally assisted switching (TAS) magnetic random access memories in FPGA design. The nonvolatility of the latter is achieved through the use of magnetic tunneling junctions (MTJs) in the MRAM cell. A thermally assisted switching scheme helps to reduce power consumption during write operation in comparison to the writing scheme in the FIMS-MTJ device. Moreover, the nonvolatility of such a design based on either an FIMS or a TAS writing scheme should reduce both power consumption and configuration time required at each power up of the circuit in comparison to classical SRAM-based FPGAs. A real-time reconfigurable (RTR) micro-FPGA using FIMS-MRAM or TAS-MRAM allows dynamic reconfiguration mechanisms, while featuring simple design architecture.

Rapid and Efficient Wet Fixation by the Use of Steam

1969 ◽  
Vol 39 (6) ◽  
pp. 497-504 ◽  
Author(s):  
Norman R. S. Hollies ◽  
Steven R. Chafitz ◽  
Karen A. Farquhar
Keyword(s):  
Fiber Surface ◽  
Strong Acid ◽  
Strength Properties ◽  
Fiber System ◽  
Resin Solution ◽  
Speed Up ◽  
Drying Properties ◽  
The Individual ◽  
Time Required ◽  
Finishing Processes

The impregnation of cotton fabrics with a solution consisting of strong acid and a combination of N-methylol resins having polymer-forming and cross-linking properties distinguishes the wet-fixation system from conventional durable-press processes, and this finish results in an improved balance of smoothness and strength properties during wear and laundering. Swelling of the fibers in a steam atmosphere, following padding in the resin solution can serve to speed up the impregnation process. In addition, with controls to minimize resin migration back to the fiber surface, steaming can substantially improve the efficiency of the use of resin for producing these smooth drying properties. The degree of penetration of resin is influenced by a number of process variables, such as predrying before steaming, steaming time, fabric tension and rapidity of neutralization. The optimum in fabric performance is achieved with both sufficient resin of both types in the fiber system and even distribution of resin within the individual fibers, Steaming acts to improve both these factors over that achieved in conventional hot wet fixation and so reduces the time required for wet fixation by a factor of 20–30. There is a corresponding increase in efficiency of resin use so that resin solids in the bath can be reduced two- to three-fold. These findings appear to have general application to a variety of cotton finishing processes involving fiber impregnation with reactive resins.

Modeling Short-Range Interactions in Concentrated Newtonian Fiber Bundle Suspensions

2021 ◽  
Vol 36 (3) ◽  
pp. 255-263
Author(s):  
N. Meyer ◽  
A. N. Hrymak ◽  
L. Kärger
Keyword(s):  
Short Range ◽  
Development Process ◽  
Contact Point ◽  
Separation Distance ◽  
Fiber Bundles ◽  
Fiber Architecture ◽  
Simulation Framework ◽  
Transient Rheology ◽  
Speed Up ◽  
Cost Efficient

Abstract Sheet Molding Compounds (SMC) offer a cost efficient way to enhance mechanical properties of a polymer with long discontinuous fibers, while maintaining formability to integrate functions, such as ribs, beads or other structural reinforcements. During SMC manufacturing, fibers remain often in a bundled configuration and the resulting fiber architecture determines part properties. Accurate prediction of this architecture by simulation of flow under consideration of the transient rheology and transient fiber orientations can speed up the development process. In particular, the interaction of bundles is of significance to predict molding pressures correctly in a direct simulation approach, which resolves individual fiber bundles. Thus, this work investigates the tangential short-range lubrication forces between fiber bundles with analytical and numerical techniques. A relation between the effective sheared gap between bundles and the bundle separation distance at the contact point is found and compared to experimental results from literature. The result is implemented in an ABAQUS contact subroutine to incorporate short-range interactions in a direct bundle simulation framework.

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