scholarly journals New Insights to Key Derivation for Tamper-Evident Physical Unclonable Functions

2019 ◽  
pp. 30-65
Author(s):  
Vincent Immler ◽  
Karthik Uppund
Keyword(s):  
Error Correcting Code ◽  
Physical Parameters ◽  
Physical Unclonable Functions ◽  
Secure Storage ◽  
Novel Approach ◽  
Cryptographic Keys ◽  
Lee Metric ◽  
Previous State ◽  
Noise Error ◽  
Key Derivation

Several publications presented tamper-evident Physical Unclonable Functions (PUFs) for secure storage of cryptographic keys and tamper-detection. Unfortunately, previously published PUF-based key derivation schemes do not sufficiently take into account the specifics of the underlying application, i.e., an attacker that tampers with the physical parameters of the PUF outside of an idealized noise error model. This is a notable extension of existing schemes for PUF key derivation, as they are typically concerned about helper data leakage, i.e., by how much the PUF’s entropy is diminished when gaining access to its helper data.To address the specifics of tamper-evident PUFs, we formalize the aspect of tamper-sensitivity, thereby providing a new tool to rate by how much an attacker is allowed to tamper with the PUF. This complements existing criteria such as effective number of secret bits for entropy and failure rate for reliability. As a result, it provides a fair comparison among different schemes and independent of the PUF implementation, as its unit is based on the noise standard deviation of the underlying PUF measurement. To overcome the limitations of previous schemes, we then propose an Error-Correcting Code (ECC) based on the Lee metric, i.e., a distance metric well-suited to describe the distance between q-ary symbols as output from an equidistant quantization, i.e., a higher-order alphabet PUF. This novel approach is required, as the underlying symbols’ bits are not i.i.d. which hinders applying previous state-of-the-art approaches. We present the concept for our scheme and demonstrate its feasibility based on an empirical PUF distribution. The benefits of our approach are an increase by over 21% in effective secret bit compared to previous approaches based on equidistant quantization. At the same time, we improve tamper-sensitivity compared to an equiprobable quantization while ensuring similar reliability and entropy. Hence, this work opens up a new direction of how to interpret the PUF output and details a practically relevant scheme outperforming all previous constructions.

Building a Trusted Environment for Security Applications

2013 ◽  
pp. 334-360
Author(s):  
Giovanni Cabiddu ◽  
Antonio Lioy ◽  
Gianluca Ramunno
Keyword(s):  
Trusted Computing ◽  
Low Cost ◽  
Computing Platform ◽  
Critical Data ◽  
Security Controls ◽  
Security Applications ◽  
Secure Storage ◽  
Cryptographic Keys ◽  
Correct Program ◽  
Trusted Platform

Security controls (such as encryption endpoints, payment gateways, and firewalls) rely on correct program execution and secure storage of critical data (such as cryptographic keys and configuration files). Even when hardware security elements are used (e.g. cryptographic accelerators) software is still—in the form of drivers and libraries—critical for secure operations. This chapter introduces the features and foundations of Trusted Computing, an architecture that exploits the low-cost TPM chip to measure the integrity of a computing platform. This allows the detection of static unauthorized manipulation of binaries (be them OS components or applications) and configuration files, hence quickly detecting software attacks. For this purpose, Trusted Computing provides enhanced security controls, such as sealed keys (that can be accessed only by good applications when the system is in a safe state) and remote attestation (securely demonstrating the software state of a platform to a remote network verifier). Besides the theoretical foundation, the chapter also guides the reader towards creation of applications that enhance their security by using the features provided by the underlying PC-class trusted platform.

scholarly journals Application of Plackett Burman and Box Behnken Design for the Optimization of a-glucosidase Production by Thermophillic Thermomyces dupontii

2020 ◽  
Vol 71 (9) ◽  
pp. 32-38
Author(s):  
Kinza Nisar ◽  
Roheena Abdullah ◽  
Afshan Kaleem ◽  
Mehwish Iqtedar ◽  
Faiza Saleem ◽  
...  
Keyword(s):  
Ammonium Sulphate ◽  
Industrial Applications ◽  
Inoculum Size ◽  
Quadratic Model ◽  
Physical Parameters ◽  
Potential Candidate ◽  
Sulphate Concentration ◽  
Novel Approach ◽  
Burman Design ◽  
Thermomyces Dupontii

A consecutive optimization based on statistical approach was applied for a-glucosidase production by both wild and mutant T. dupontii. Plackett Burman design (PBD) with two levels was employed in order to screen the significant effect of different nutritional and physical parameters through submerged fermentation. Among all nine variables tested in PBD, incubation time, inoculum size and ammonium sulphate concentration were selected. The Box-Behnken approach was further applied for process optimization. The a-glucosidase production for both wild and mutant T.dupontii was obtained at 72 h of incubation, 1.25 mL inoculum size and 0.25% ammonium sulphate concentration with relatively 95% correlation between the experimentally predicted and observed values. The duration of maximum enzyme production in RSM was cost-saving and fast. The quadratic model was in satisfactory adjustment with the experimental data with high R2 value which describes 98.90% of response variability of the model. Moreover, the novel approach of this present work is that, consecutive optimization were applied for maximum a-glucosidase production using response surface methodology by both wild and mutant thermophillic T. dupontii. Results revealed that thermophillic mutant T. dupontii could be potential candidate for industrial applications.

scholarly journals Towards memory integrity and authenticity of multi-processors system-on-chip using physical unclonable functions

2019 ◽  
Vol 61 (1) ◽  
pp. 29-43
Author(s):  
Johanna Sepúlveda ◽  
Felix Wilgerodt ◽  
Michael Pehl
Keyword(s):  
New Technologies ◽  
System On Chip ◽  
Authentication Code ◽  
Physical Unclonable Functions ◽  
Persistent Problem ◽  
Injection Attacks ◽  
Cryptographic Keys ◽  
Memory Content ◽  
And Performance ◽  
On Chip

Abstract A persistent problem for modern Multi-Processors System-on-Chip (MPSoCs) is their vulnerability to code injection attacks. By tampering the memory content, attackers are able to extract secrets from the MPSoC and to modify or deny the MPSoC’s operation. This work proposes SEPUFSoC (Secure PUF-based SoC), a novel flexible, secure, and fast architecture able to be integrated into any MPSoC. SEPUFSoC prevents execution of unauthorized code as well as data manipulation by ensuring memory integrity and authentication. SEPUFSoC achieves: i) efficiency, through the integration of a fast and lightweight hash function for Message Authentication Code (MAC) generation and integrity verification of the memory lines at runtime; and ii) lightweight security, through the use of a Physical Unclonable Function (PUF) to securely generate and store the cryptographic keys that are used for the application authentication. We discuss the security and performance of SEPUFSoC for single core and multi-core systems. Results show that the SEPUFSoC is a secure, fast, and low overhead solution for MPSoCs. We discuss the SEPUFSoC security and cost, which strongly depends on the PUF and hash selection. In the future, new technologies may allow the exploration of different PUFs.

scholarly journals Post Quantum Cryptographic Keys Generated with Physical Unclonable Functions

2021 ◽  
Vol 11 (6) ◽  
pp. 2801
Author(s):  
Bertrand Cambou ◽  
Michael Gowanlock ◽  
Bahattin Yildiz ◽  
Dina Ghanaimiandoab ◽  
Kaitlyn Lee ◽  
...  
Keyword(s):  
High Performance ◽  
Public Key ◽  
Graphic Processing Units ◽  
Public And Private ◽  
Physical Unclonable Functions ◽  
The Public ◽  
Cryptographic Keys ◽  
Network Technologies ◽  
And Storage ◽  
Performance Computing

Lattice and code cryptography can replace existing schemes such as elliptic curve cryptography because of their resistance to quantum computers. In support of public key infrastructures, the distribution, validation and storage of the cryptographic keys is then more complex for handling longer keys. This paper describes practical ways to generate keys from physical unclonable functions, for both lattice and code-based cryptography. Handshakes between client devices containing the physical unclonable functions (PUFs) and a server are used to select sets of addressable positions in the PUFs, from which streams of bits called seeds are generated on demand. The public and private cryptographic key pairs are computed from these seeds together with additional streams of random numbers. The method allows the server to independently validate the public key generated by the PUF, and act as a certificate authority in the network. Technologies such as high performance computing, and graphic processing units can further enhance security by preventing attackers from making this independent validation when only equipped with less powerful computers.

scholarly journals LMPred: Predicting Antimicrobial Peptides Using Pre-Trained Language Models and Deep Learning

2021 ◽  
Author(s):  
William Dee
Keyword(s):  
Antimicrobial Peptides ◽  
Bacterial Resistance ◽  
State Of The Art ◽  
Language Models ◽  
Peptide Sequence ◽  
Independent Dataset ◽  
Therapeutic Drugs ◽  
Novel Approach ◽  
Previous State ◽  
In Silico Models

Antimicrobial peptides (AMPs) are increasingly being used in the development of new therapeutic drugs, in areas such as cancer therapy and hypertension. Additionally, they are seen as an alternative to antibiotics due to the increasing occurrence of bacterial resistance. Wet-laboratory experimental identification, however, is both time consuming and costly, so in-silico models are now commonly used in order to screen new AMP candidates. This paper proposes a novel approach of creating model inputs; using pre-trained language models to produce contextualized embeddings representing the amino acids within each peptide sequence, before a convolutional neural network is then trained as the classifier. The optimal model was validated on two datasets, being one previously used in AMP prediction research, and an independent dataset, created by this paper. Predictive accuracies of 93.33% and 88.26% were achieved respectively, outperforming all previous state-of-the-art classification models.

scholarly journals Machine Learning of Physical Unclonable Functions using Helper Data

2021 ◽  
pp. 1-36
Author(s):  
Emanuele Strieder ◽  
Christoph Frisch ◽  
Michael Pehl
Keyword(s):  
Machine Learning ◽  
Error Correcting Code ◽  
Public Information ◽  
Block Codes ◽  
Mitigation Strategies ◽  
Direct Access ◽  
Physical Unclonable Functions ◽  
Repetition Code ◽  
The Impact ◽  
Helper Data

Physical Unclonable Functions (PUFs) are used in various key-generation schemes and protocols. Such schemes are deemed to be secure even for PUFs with challenge-response behavior, as long as no responses and no reliability information about the PUF are exposed. This work, however, reveals a pitfall in these constructions: When using state-of-the-art helper data algorithms to correct noisy PUF responses, an attacker can exploit the publicly accessible helper data and challenges. We show that with this public information and the knowledge of the underlying error correcting code, an attacker can break the security of the system: The redundancy in the error correcting code reveals machine learnable features and labels. Learning these features and labels results in a predictive model for the dependencies between different challenge-response pairs (CRPs) without direct access to the actual PUF response. We provide results based on simulated data of a k-SUM PUF model and an Arbiter PUF model. We also demonstrate the attack for a k-SUM PUF model generated from real data and discuss the impact on more recent PUF constructions such as the Multiplexer PUF and the Interpose PUF. The analysis reveals that especially the frequently used repetition code is vulnerable: For a SUM-PUF in combination with a repetition code, e.g., already the observation of 800 challenges and helper data bits suffices to reduce the entropy of the key down to one bit. The analysis also shows that even other linear block codes like the BCH, the Reed-Muller, or the Single Parity Check code are affected by the problem. The code-dependent insights we gain from the analysis allow us to suggest mitigation strategies for the identified attack. While the shown vulnerability advances Machine Learning (ML) towards realistic attacks on key-storage systems with PUFs, our analysis also facilitates a better understanding and evaluation of existing approaches and protocols with PUFs. Therefore, it brings the community one step closer to a more complete leakage assessment of PUFs.

Secure Storage of Cryptographic Keys within Random Volumetric Materials

CLEO: 2013 ◽  
2013 ◽  
Author(s):  
Roarke Horstmeyer ◽  
Benjamin Judkewitz ◽  
Ivo Vellekoop ◽  
Changhuei Yang
Keyword(s):  
Secure Storage ◽  
Cryptographic Keys
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