3 research outputs found
Q-Class Authentication System for Double Arbiter PUF
Physically Unclonable Function (PUF) is a cryptographic primitive that is based on physical property of each entity or Integrated Circuit (IC) chip. It is expected that PUF be used in security applications such as ID generation and authentication. Some responses from PUF are unreliable, and they are usually discarded. In this paper, we propose a new PUF-based authentication system that exploits information of unreliable responses. In the proposed method, each response is categorized into multiple classes by its unreliability evaluated by feeding the same challenges several times. This authentication system is named Q-class authentication, where Q is the number of classes. We perform experiments assuming a challenge-response authentication system with a certain threshold of errors. Considering 4-class separation for 4-1 Double Arbiter PUF, it is figured out that the advantage of a legitimate prover against a clone is improved form 24% to 36% in terms of success rate. In other words, it is possible to improve the tolerance of machine-learning attack by using unreliable information that was previously regarded disadvantageous to authentication systems
Comprehensive Designs of Innovate Secure Hardware Devices against Machine Learning Attacks and Power Analysis Attacks
Hardware security is an innovate subject oriented from growing demands of cybersecurity and new information vulnerabilities from physical leakages on hardware devices. However, the mainstream of hardware manufacturing industry is still taking benefits of products and the performance of chips as priority, restricting the design of hardware secure countermeasures under a compromise to a finite expense of overheads. Consider the development trend of hardware industries and state-of-the-art researches of architecture designs, this dissertation proposes some new physical unclonable function (PUF) designs as countermeasures to side-channel attacks (SCA) and machine learning (ML) attacks simultaneously. Except for the joint consideration of hardware and software vulnerabilities, those designs also take efficiencies and overhead problems into consideration, making the new-style of PUF more possible to be merged into current chips as well as their design concepts. While the growth of artificial intelligence and machine-learning techniques dominate the researching trends of Internet of things (IoT) industry, some mainstream architectures of neural networks are implemented as hypothetical attacking model, whose results are used as references for further lifting the performance, the security level, and the efficiency in lateral studies. In addition, a study of implementation of neural networks on hardware designs is proposed, this realized the initial attempt to introduce AI techniques to the designs of voltage regulation (VR). All aforementioned works are demonstrated to be of robustness to threats with corresponding power attack tests or ML attack tests. Some conceptional models are proposed in the last of the dissertation as future plans so as to realize secure on-chip ML models and hardware countermeasures to hybrid threats
Comprehensive study of physical unclonable functions on FPGAs: correlation driven Implementation, deep learning modeling attacks, and countermeasures
For more than a decade and a half, Physical Unclonable Functions (PUFs) have been
presented as a promising hardware security primitive. The idea of exploiting variabilities
in hardware fabrication to generate a unique fingerprint for every silicon chip introduced a
more secure and cheaper alternative. Other solutions using non-volatile memory to store
cryptographic keys, require additional processing steps to generate keys externally, and
secure environments to exchange generated keys, which introduce many points of attack
that can be used to extract the secret keys.
PUFs were addressed in the literature from different perspectives. Many publications
focused on proposing new PUF architectures and evaluation metrics to improve security
properties like response uniqueness per chip, response reproducibility of the same PUF
input, and response unpredictability using previous input/response pairs. Other research
proposed attack schemes to clone the response of PUFs, using conventional machine learning
(ML) algorithms, side-channel attacks using power and electromagnetic traces, and fault
injection using laser beams and electromagnetic pulses. However, most attack schemes to
be successful, imposed some restrictions on the targeted PUF architectures, which make
it simpler and easier to attack. Furthermore, they did not propose solid and provable
enhancements on these architectures to countermeasure the attacks. This leaves many
open questions concerning how to implement perfect secure PUFs especially on FPGAs,
how to extend previous modeling attack schemes to be successful against more complex
PUF architectures (and understand why modeling attacks work) and how to detect and
countermeasure these attacks to guarantee that secret data are safe from the attackers.
This Ph.D. dissertation contributes to the state of the art research on physical unclonable
functions in several ways. First, the thesis provides a comprehensive analysis of the implementation of secure PUFs on FPGAs using manual placement and manual routing
techniques guided by new performance metrics to overcome FPGAs restrictions with minimum
hardware and area overhead. Then the impact of deep learning (DL) algorithms is
studied as a promising modeling attack scheme against complex PUF architectures, which
were reported immune to conventional (ML) techniques. Furthermore, it is shown that
DL modeling attacks successfully overcome the restrictions imposed by previous research
even with the lack of accurate mathematical models of these PUF architectures. Finally,
this comprehensive analysis is completed by understanding why deep learning attacks are
successful and how to build new PUF architectures and extra circuitry to thwart these types
of attacks. This research is important for deploying cheap and efficient hardware security
primitives in different fields, including IoT applications, embedded systems, automotive
and military equipment. Additionally, it puts more focus on the development of strong intrinsic PUFs which are widely proposed and deployed in many security protocols used
for authentication, key establishment, and Oblivious transfer protocols