128 research outputs found
Differential Fault Analysis Automation
Characterization of all possible faults in a cryptosystem exploitable for fault attacks is a problem
which is of both theoretical and practical interest for the cryptographic community. The complete
knowledge of exploitable fault space is desirable while designing optimal countermeasures for any
given crypto-implementation. In this paper, we address the exploitable fault characterization problem
in the context of Differential Fault Analysis (DFA) attacks on block ciphers. The formidable size
of the fault spaces demands an automated albeit fast mechanism for verifying each individual fault
instance and neither the traditional, cipher-specific, manual DFA techniques nor the generic and au-
tomated Algebraic Fault Attacks (AFA) [10] fulfill these criteria. Further, the diversified structures
of different block ciphers suggest that such an automation should be equally applicable to any block
cipher. This work presents an automated framework for DFA identification, fulfilling all aforemen-
tioned criteria, which, instead of performing the attack just estimates the attack complexity for each
individual fault instance. A generic and extendable data-mining assisted dynamic analysis frame-
work capable of capturing a large class of DFA distinguishers is devised, along with a graph-based
complexity analysis scheme. The framework significantly outperforms another recently proposed
one [6], in terms of attack class coverage and automation effort. Experimental evaluation on AES and
PRESENT establishes the effectiveness of the proposed framework in detecting most of the known
DFAs, which eventually enables the characterization of the exploitable fault space
ExpFault: An Automated Framework for Exploitable Fault Characterization in Block Ciphers (Revised Version)
Malicious exploitation of faults for extracting secrets is one of the most practical and potent threats to modern cryptographic primitives. Interestingly, not every possible fault for a cryptosystem is maliciously exploitable, and evaluation of the exploitability of a fault is nontrivial. In order to devise precise defense mechanisms against such rogue faults, a comprehensive knowledge is required about the exploitable part of the fault space of a cryptosystem.
Unfortunately, the fault space is diversified and of formidable size even while a single crypto-primitive is considered and traditional manual fault analysis techniques may often fall short
to practically cover such a fault space within reasonable time. An automation for analyzing individual fault instances for their exploitability is thus inevitable. Such an automation is
supposed to work as the core engine for analyzing the fault spaces of cryptographic primitives. In this paper, we propose an automation for evaluating the exploitability status of fault instances
from block ciphers, mainly in the context of Differential Fault Analysis (DFA) attacks. The proposed framework is generic and scalable, which are perhaps the two most important features
for covering diversified fault spaces of formidable size originating from different ciphers. As a proof-of-concept, we reconstruct some known attack examples on AES and PRESENT using
the framework and finally analyze a recently proposed cipher GIFT [BPP + 17] for the first time. It is found that the secret key of GIFT can be determined with 2 nibble fault instances injected
consecutively at the beginning of the 25th and 23rd round with remaining key space complexity of 2^7.06
Automatic Characterization of Exploitable Faults: A Machine Learning Approach
Characterization of the fault space of a cipher to filter out
a set of faults potentially exploitable for fault attacks (FA), is a prob-
lem with immense practical value. A quantitative knowledge of the ex-
ploitable fault space is desirable in several applications, like security
evaluation, cipher construction and implementation, design, and test-
ing of countermeasures etc. In this work, we investigate this problem in
the context of block ciphers. The formidable size of the fault space of
a block cipher mandates the use of an automation to solve this prob-
lem, which should be able to characterize each individual fault instance
quickly. On the other hand, the automation is expected to be applicable
to most of the block cipher constructions. Existing techniques for au-
tomated fault attacks do not satisfy both of these goals simultaneously
and hence are not directly applicable in the context of exploitable fault
characterization. In this paper, we present a supervised machine learning
(ML) assisted automated framework, which successfully addresses both
of the criteria mentioned. The key idea is to extrapolate the knowledge of
some existing FAs on a cipher to rapidly figure out new attack instances
on the same. Experimental validation of the proposed framework on two
state-of-the-art block ciphers – PRESENT and LED, establishes that our
approach is able to provide fairly good accuracy in identifying exploitable
fault instances at a reasonable cost. Finally, the effect of different S-Boxes
on the fault space of a cipher is evaluated utilizing the framework
Vulnerability Assessment of Ciphers To Fault Attacks Using Reinforcement Learning
A fault attack (FA) is one of the most potent threats to cryptographic applications. Implementing a FA-protected block cipher requires knowledge of the exploitable fault space of the underlying crypto algorithm. The discovery of exploitable faults is a challenging problem that demands human expertise and time. Current practice is to rely on certain predefined fault models. However, the applicability of such fault models varies among ciphers. Prior work discovers such exploitable fault models individually for each cipher at the expanse of a large amount of human effort. Our work completely replaces human effort by using reinforcement learning (RL) over the huge fault space of a block cipher to discover the effective fault models automatically. Validation on an AES block cipher demonstrates that our approach can automatically discover the effective fault models within a few hours, outperforming prior work, which requires days of manual analysis. The proposed approach also reveals vulnerabilities in the existing FA-protected block ciphers and initiates an end-to-end vulnerability assessment flow
SoK: Assisted Fault Simulation - Existing Challenges and Opportunities Offered by AI
Fault injection attacks have caused implementations to behave unexpectedly, resulting in a spectacular bypass of security features and even the extraction of cryptographic keys. Clearly, developers want to ensure the robustness of the software against faults and eliminate production weaknesses that could lead to exploitation. Several fault simulators have been released that promise cost-effective evaluations against fault attacks. In this paper, we set out to discover how suitable such tools are, for a developer who wishes to create robust software against fault attacks. We found four open-source fault simulators that employ different techniques to navigate faults, which we objectively compare and discuss their benefits and drawbacks. Unfortunately, none of the four open-source fault simulators employ artificial intelligence (AI) techniques. However, AI was successfully applied to improve the fault simulation of cryptographic algorithms, though none of these tools is open source. We suggest improvements to open-source fault simulators inspired by the AI techniques used by cryptographic fault simulators
Fault Attacks In Symmetric Key Cryptosystems
Fault attacks are among the well-studied topics in the area of cryptography. These attacks constitute a powerful tool to recover the secret key used in the encryption process. Fault attacks work by forcing a device to work under non-ideal environmental conditions (such as high temperature) or external disturbances (such as glitch in the power supply) while performing a cryptographic operation. The recent trend shows that the amount of research in this direction; which ranges from attacking a particular primitive, proposing a fault countermeasure, to attacking countermeasures; has grown up substantially and going to stay as an active research interest for a foreseeable future. Hence, it becomes apparent to have a comprehensive yet compact study of the (major) works. This work, which covers a wide spectrum in the present day research on fault attacks that fall under the purview of the symmetric key cryptography, aims at fulfilling the absence of an up-to-date survey. We present mostly all aspects of the topic in a way which is not only understandable for a non-expert reader, but also helpful for an expert as a reference
Security in 1-wire system : case study : Home automation /
La automatización de viviendas es un campo de la tecnología que siempre se encuentra en crecimiento, desarrollando sistemas que reducen los costos de los dispositivos.
Por esto, se ha logrado que la domótica esté al alcance de todos. Desde la aparición
de productos que permiten crear tu propio sistema domótico, y la reciente popularidad que ha tenido el Internet de las cosas (IoT), la industria de la automatización
de viviendas ha cambiado mucho. Tener la habilidad de controlar dispositivos a
través de Internet crea numerosas vulnerabilidades al sistema, permitiendo a un atacante controlar y ver todo lo que ocurre. En este trabajo se estudia un sistema
domótico que usa 1-wire como protocolo de comunicación. Originalmente, el sistema
carece de seguridad. Nuestro objetivo es implementar seguridad de la información a
través de la encriptación de los comandos del sistema, para así poder proveer Confidencialidad, Integridad y Disponibilidad (CIA). Los resultados muestran no sólo la
implementación exitosa del módulo criptográfico dentro del sistema domótico para
proveer seguridad, sino que también se demuestra que añadir este proceso no afectaría
el modo en que el usuario maneja sus dispositivos.Incluye referencias bibliográfica
An Automated and Scalable Formal Process for Detecting Fault Injection Vulnerabilities in Binaries
Fault injection has increasingly been used both to attack software applications, and to test system robustness. Detecting fault injection vulnerabilities has been approached with a variety of different but limited methods. This paper proposes an extension of a recently published general model checking based process to detect fault injection vulnerabilities in binaries. This new extension makes the general process scalable to real-world implementions which is demonstrated by detecting vulnerabilities in different cryptographic implementations
Exploitation of Unintentional Information Leakage from Integrated Circuits
Unintentional electromagnetic emissions are used to recognize or verify the identity of a unique integrated circuit (IC) based on fabrication process-induced variations in a manner analogous to biometric human identification. The effectiveness of the technique is demonstrated through an extensive empirical study, with results presented indicating correct device identification success rates of greater than 99:5%, and average verification equal error rates (EERs) of less than 0:05% for 40 near-identical devices. The proposed approach is suitable for security applications involving commodity commercial ICs, with substantial cost and scalability advantages over existing approaches. A systematic leakage mapping methodology is also proposed to comprehensively assess the information leakage of arbitrary block cipher implementations, and to quantitatively bound an arbitrary implementation\u27s resistance to the general class of differential side channel analysis techniques. The framework is demonstrated using the well-known Hamming Weight and Hamming Distance leakage models, and approach\u27s effectiveness is demonstrated through the empirical assessment of two typical unprotected implementations of the Advanced Encryption Standard. The assessment results are empirically validated against correlation-based differential power and electromagnetic analysis attacks
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