Modeling attacks on physical unclonable functions

We show in this paper how several proposed Physical Unclonable Functions (PUFs) can be broken by numerical modeling attacks. Given a set of challenge-response pairs (CRPs) of a PUF, our attacks construct a computer algorithm which behaves indistinguishably from the original PUF on almost all CRPs. This algorithm can subsequently impersonate the PUF, and can be cloned and distributed arbitrarily. This breaks the security of essentially all applications and protocols that are based on the respective PUF. The PUFs we attacked successfully include standard Arbited PUFs and Ring Oscillator PUFs of arbitrary sizes, and XO Arbiter PUFs, Lightweight Secure PUFs, and Feed-Forward Arbiter PUFs of up to a given size and complexity. Our attacks are based upon various machine learning techniques including Logistic Regression and Evolution Strategies. Our work leads to new design requirements for secure electrical PUFs, and will be useful to PUF designers and attackers alike.Technische Universitat Munche

28 th European Photovoltaic Solar Energy Conference, 30

ABSTRACT: As grid price rises and the feed-in tariff declines, the economics of local storage become increasingly lucrative to the system owner. The attractiveness of a local storage investment is compounded in the presence of a PV grid injection cap. The larger the PV system size is relative to this cap level, the greater the opportunity exists to charge the local storage with PV production that would otherwise be dissipated without credit. This study utilizes two household demand profiles that represent the extremes of the potential for local PV self-consumption and, consequently, the range of economic potential that exists for local storage to be coupled with residential PV systems. A series of algorithms were subsequently developed to analyze the related benefit potential of delayed storage charging to target instances of excess PV production depending upon the grid injection cap

PUF Modeling Attacks on Simulated and Silicon Data

We discuss numerical modeling attacks on several proposed strong physical unclonable functions (PUFs). Given a set of challenge-response pairs (CRPs) of a Strong PUF, the goal of our attacks is to construct a computer algorithm which behaves indistinguishably from the original PUF on almost all CRPs. If successful, this algorithm can subsequently impersonate the Strong PUF, and can be cloned and distributed arbitrarily. It breaks the security of any applications that rest on the Strong PUF's unpredictability and physical unclonability. Our method is less relevant for other PUF types such as Weak PUFs. The Strong PUFs that we could attack successfully include standard Arbiter PUFs of essentially arbitrary sizes, and XOR Arbiter PUFs, Lightweight Secure PUFs, and Feed-Forward Arbiter PUFs up to certain sizes and complexities. We also investigate the hardness of certain Ring Oscillator PUF architectures in typical Strong PUF applications. Our attacks are based upon various machine learning techniques, including a specially tailored variant of logistic regression and evolution strategies. Our results are mostly obtained on CRPs from numerical simulations that use established digital models of the respective PUFs. For a subset of the considered PUFs-namely standard Arbiter PUFs and XOR Arbiter PUFs-we also lead proofs of concept on silicon data from both FPGAs and ASICs. Over four million silicon CRPs are used in this process. The performance on silicon CRPs is very close to simulated CRPs, confirming a conjecture from earlier versions of this work. Our findings lead to new design requirements for secure electrical Strong PUFs, and will be useful to PUF designers and attackers alike.National Science Foundation (U.S.) (Grant CNS 0923313)National Science Foundation (U.S.) (Grant CNS 0964641

Interaction specificity of Arabidopsis 14-3-3 proteins with phototropin receptor kinases

Phototropin receptor kinases play an important roles in optimising plant growth in response to blue light. Much is known regarding their photochemical reactivity, yet little progress has been made to identify downstream signalling components. Here, we isolated several interacting proteins for Arabidopsis phototropin 1 (phot1) by yeast two-hybrid screening. These include members of the NPH3/RPT2 (NRL) protein family, proteins associated with vesicle trafficking, and the 14-3-3 lambda (?) isoform from Arabidopsis . 14-3-3? and phot1 were found to colocalise and interact in vivo. Moreover, 14-3-3 binding to phot1 was limited to non-epsilon 14-3-3 isoforms and was dependent on key sites of receptor autophosphorylation. No 14-3-3 binding was detected for Arabidopsis phot2, suggesting that 14-3-3 proteins represent specific mode of phot1 signalling

Probabilistic short term wind power forecasts using deep neural networks with discrete target classes

Usually, neural networks trained on historical feed-in time series of wind turbines deterministically predict power output over the next hours to days. Here, the training goal is to minimise a scalar cost function, often the root mean square error (RMSE) between network output and target values. Yet similar to the analog ensemble (AnEn) method, the training algorithm can also be adapted to analyse the uncertainty of the power output from the spread of possible targets found in the historical data for a certain meteorological situation. In this study, the uncertainty estimate is achieved by discretising the continuous time series of power targets into several bins (classes). For each forecast horizon, a neural network then predicts the probability of power output falling into each of the bins, resulting in an empirical probability distribution. Similiar to the AnEn method, the proposed method avoids the use of costly numerical weather prediction (NWP) ensemble runs, although a selection of several deterministic NWP forecasts as input is helpful. Using state-of-the-art deep learning technology, we applied our method to a large region and a single wind farm. MAE scores of the 50-percentile were on par with or better than comparable deterministic forecasts. The corresponding Continuous Ranked Probability Score (CRPS) was even lower. Future work will investigate the overdispersiveness sometimes observed, and extend the method to solar power forecasts.</p

Lightweight and Secure PUF Key Storage Using Limits of Machine Learning

13th International Workshop, Nara, Japan, September 28 – October 1, 2011. ProceedingsA lightweight and secure key storage scheme using silicon Physical Unclonable Functions (PUFs) is described. To derive stable PUF bits from chip manufacturing variations, a lightweight error correction code (ECC) encoder / decoder is used. With a register count of 69, this codec core does not use any traditional error correction techniques and is 75% smaller than a previous provably secure implementation, and yet achieves robust environmental performance in 65nm FPGA and 0.13μ ASIC implementations. The security of the syndrome bits uses a new security argument that relies on what cannot be learned from a machine learning perspective. The number of Leaked Bits is determined for each Syndrome Word, reducible using Syndrome Distribution Shaping. The design is secure from a min-entropy standpoint against a machine-learning-equipped adversary that, given a ceiling of leaked bits, has a classification error bounded by ε. Numerical examples are given using latest machine learning results

Expression of the maize ZmGF14-6 gene in rice confers tolerance to drought stress while enhancing susceptibility to pathogen infection

14-3-3 proteins are found in all eukaryotes where they act as regulators of diverse signalling pathways associated with a wide range of biological processes. In this study the functional characterization of the ZmGF14-6 gene encoding a maize 14-3-3 protein is reported. Gene expression analyses indicated that ZmGF14-6 is up-regulated by fungal infection and salt treatment in maize plants, whereas its expression is down-regulated by drought stress. It is reported that rice plants constitutively expressing ZmGF14-6 displayed enhanced tolerance to drought stress which was accompanied by a stronger induction of drought-associated rice genes. However, rice plants expressing ZmGF14-6 either in a constitutive or under a pathogen-inducible regime showed a higher susceptibility to infection by the fungal pathogens Fusarium verticillioides and Magnaporthe oryzae. Under infection conditions, a lower intensity in the expression of defence-related genes occurred in ZmGF14-6 rice plants. These findings support that ZmGF14-6 positively regulates drought tolerance in transgenic rice while negatively modulating the plant defence response to pathogen infection. Transient expression assays of fluorescently labelled ZmGF14-6 protein in onion epidermal cells revealed a widespread distribution of ZmGF14-6 in the cytoplasm and nucleus. Additionally, colocalization experiments of fluorescently labelled ZmGF14-6 with organelle markers, in combination with cell labelling with the endocytic tracer FM4-64, revealed a subcellular localization of ZmGF14-6 in the early endosomes. Taken together, these results improve our understanding of the role of ZmGF14-6 in stress signalling pathways, while indicating that ZmGF14-6 inversely regulates the plant response to biotic and abiotic stresses

Model-based contextual policy search for data-efficient generalization of robot skills

In robotics, lower-level controllers are typically used to make the robot solve a specific task in a fixed context. For example, the lower-level controller can encode a hitting movement while the context defines the target coordinates to hit. However, in many learning problems the context may change between task executions. To adapt the policy to a new context, we utilize a hierarchical approach by learning an upper-level policy that generalizes the lower-level controllers to new contexts. A common approach to learn such upper-level policies is to use policy search. However, the majority of current contextual policy search approaches are model-free and require a high number of interactions with the robot and its environment. Model-based approaches are known to significantly reduce the amount of robot experiments, however, current model-based techniques cannot be applied straightforwardly to the problem of learning contextual upper-level policies. They rely on specific parametrizations of the policy and the reward function, which are often unrealistic in the contextual policy search formulation. In this paper, we propose a novel model-based contextual policy search algorithm that is able to generalize lower-level controllers, and is data-efficient. Our approach is based on learned probabilistic forward models and information theoretic policy search. Unlike current algorithms, our method does not require any assumption on the parametrization of the policy or the reward function. We show on complex simulated robotic tasks and in a real robot experiment that the proposed learning framework speeds up the learning process by up to two orders of magnitude in comparison to existing methods, while learning high quality policies

Deep Reinforcement Learning: An Overview

In recent years, a specific machine learning method called deep learning has gained huge attraction, as it has obtained astonishing results in broad applications such as pattern recognition, speech recognition, computer vision, and natural language processing. Recent research has also been shown that deep learning techniques can be combined with reinforcement learning methods to learn useful representations for the problems with high dimensional raw data input. This chapter reviews the recent advances in deep reinforcement learning with a focus on the most used deep architectures such as autoencoders, convolutional neural networks and recurrent neural networks which have successfully been come together with the reinforcement learning framework.Comment: Proceedings of SAI Intelligent Systems Conference (IntelliSys) 201

Interactome analysis of the six cotton 14-3-3s that are preferentially expressed in fibres and involved in cell elongation

Proteins of the 14-3-3 family regulate a divergent set of signalling pathways in all eukaryotic organisms. In this study, several cDNAs encoding 14-3-3 proteins were isolated from a cotton fibre cDNA library. The Gh14-3-3 genes share high sequence homology at the nucleotide level in the coding region and at the amino acid level. Real-time quantitative RT-PCR analysis indicated that the expression of these Gh14-3-3 genes is developmentally regulated in fibres, and reached their peak at the stage of rapid cell elongation of fibre development. Furthermore, overexpression of Gh14-3-3a, Gh14-3-3e, and Gh14-3-3L in fission yeast promoted atypical longitudinal growth of the host cells. Yeast two-hybrid analysis revealed that the interaction between cotton 14-3-3 proteins is isoform selective. Through yeast two-hybrid screening, 38 novel interaction partners of the six 14-3-3 proteins (Gh14-3-3a, Gh14-3-3e, Gh14-3-3f, Gh14-3-3g, Gh14-3-3h, and Gh14-3-3L), which are involved in plant development, metabolism, signalling transduction, and other cellular processes, were identified in cotton fibres. Taking these data together, it is proposed that the Gh14-3-3 proteins may participate in regulation of fibre cell elongation. Thus, the results of this study provide novel insights into the 14-3-3 signalling related to fibre development of cotton