509 research outputs found
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
Towards Multidimensional Verification: Where Functional Meets Non-Functional
Trends in advanced electronic systems' design have a notable impact on design
verification technologies. The recent paradigms of Internet-of-Things (IoT) and
Cyber-Physical Systems (CPS) assume devices immersed in physical environments,
significantly constrained in resources and expected to provide levels of
security, privacy, reliability, performance and low power features. In recent
years, numerous extra-functional aspects of electronic systems were brought to
the front and imply verification of hardware design models in multidimensional
space along with the functional concerns of the target system. However,
different from the software domain such a holistic approach remains
underdeveloped. The contributions of this paper are a taxonomy for
multidimensional hardware verification aspects, a state-of-the-art survey of
related research works and trends towards the multidimensional verification
concept. The concept is motivated by an example for the functional and power
verification dimensions.Comment: 2018 IEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP
and International Symposium of System-on-Chip (SoC
CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information
Machine learning has become mainstream across industries. Numerous examples
proved the validity of it for security applications. In this work, we
investigate how to reverse engineer a neural network by using only power
side-channel information. To this end, we consider a multilayer perceptron as
the machine learning architecture of choice and assume a non-invasive and
eavesdropping attacker capable of measuring only passive side-channel leakages
like power consumption, electromagnetic radiation, and reaction time.
We conduct all experiments on real data and common neural net architectures
in order to properly assess the applicability and extendability of those
attacks. Practical results are shown on an ARM CORTEX-M3 microcontroller. Our
experiments show that the side-channel attacker is capable of obtaining the
following information: the activation functions used in the architecture, the
number of layers and neurons in the layers, the number of output classes, and
weights in the neural network. Thus, the attacker can effectively reverse
engineer the network using side-channel information.
Next, we show that once the attacker has the knowledge about the neural
network architecture, he/she could also recover the inputs to the network with
only a single-shot measurement. Finally, we discuss several mitigations one
could use to thwart such attacks.Comment: 15 pages, 16 figure
Understanding multidimensional verification: Where functional meets non-functional
Abstract Advancements in electronic systems' design have a notable impact on design verification technologies. The recent paradigms of Internet-of-Things (IoT) and Cyber-Physical Systems (CPS) assume devices immersed in physical environments, significantly constrained in resources and expected to provide levels of security, privacy, reliability, performance and low-power features. In recent years, numerous extra-functional aspects of electronic systems were brought to the front and imply verification of hardware design models in multidimensional space along with the functional concerns of the target system. However, different from the software domain such a holistic approach remains underdeveloped. The contributions of this paper are a taxonomy for multidimensional hardware verification aspects, a state-of-the-art survey of related research works and trends enabling the multidimensional verification concept. Further, an initial approach to perform multidimensional verification based on machine learning techniques is evaluated. The importance and challenge of performing multidimensional verification is illustrated by an example case study
PDNPulse: Sensing PCB Anomaly with the Intrinsic Power Delivery Network
The ubiquitous presence of printed circuit boards (PCBs) in modern electronic
systems and embedded devices makes their integrity a top security concern. To
take advantage of the economies of scale, today's PCB design and manufacturing
are often performed by suppliers around the globe, exposing them to many
security vulnerabilities along the segmented PCB supply chain. Moreover, the
increasing complexity of the PCB designs also leaves ample room for numerous
sneaky board-level attacks to be implemented throughout each stage of a PCB's
lifetime, threatening many electronic devices. In this paper, we propose
PDNPulse, a power delivery network (PDN) based PCB anomaly detection framework
that can identify a wide spectrum of board-level malicious modifications.
PDNPulse leverages the fact that the PDN's characteristics are inevitably
affected by modifications to the PCB, no matter how minuscule. By detecting
changes to the PDN impedance profile and using the Frechet distance-based
anomaly detection algorithms, PDNPulse can robustly and successfully discern
malicious modifications across the system. Using PDNPulse, we conduct extensive
experiments on seven commercial-off-the-shelf PCBs, covering different design
scales, different threat models, and seven different anomaly types. The results
confirm that PDNPulse creates an effective security asymmetry between attack
and defense
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