588 research outputs found
Adaptive Synchronization of Complex Dynamical Networks with State Predictor
This paper addresses the adaptive synchronization of complex dynamical networks with nonlinear dynamics. Based on the Lyapunov method, it is shown that the network can synchronize to the synchronous state by introducing local adaptive strategy to the coupling strengths. Moreover, it is also proved that the convergence speed of complex dynamical networks can be increased via designing a state predictor. Finally, some numerical simulations are worked out to illustrate the analytical results
Emerging Opportunities and Challenges in Hardware Security
Recent years have seen the rapid development of many emerging technologies in various aspects of computer engineering, such as new devices, new fabrication techniques of integrated circuits (IC), new computation frameworks, etc.
In this dissertation, we study the security challenges to these emerging technologies as well as the security opportunities they bring. Specifically, we investigate the security opportunities in double patterning lithography, the security challenges in physical unclonable functions, and security issues in machine learning.
Double patterning lithography (DPL) is an emerging fabrication technique for ICs. We study the security opportunities that DPL brings at the layout level. DPL is used to set up two independent mask development lines which do not need to share any information. Under this setup, we consider the attack model where the untrusted employee(s) who has access to only one mask may try to infer the entire circuit design or insert additional malicious circuitry into the design. As a countermeasure, we customize DPL to decompose the layout into two sub-layouts in such a way that each sub-layout individually exposes minimum information about the other and hence protects the entire layout from any untrusted personnel.
Physical unclonable functions (PUF) are a type of circuits for which each copy (of the same circuit structure) has a unique and unpredictable functionality.
The unpredictable behavior is caused by the manufacturing variations of electronic devices. However, for many state-of-the-art PUF designs, we show that the device variations can be estimated using an optimization-theoretic formulation and hence the PUF's input-output behavior becomes predictable. Simulations show a substantial reduction in attack complexity compared to previously proposed machine learning based attacks.
Neural network (NN) is an emerging computation framework for machine learning (ML). It is increasingly popular for system developers to use pre-trained NN models instead of training their own because training is painstaking and sometimes requires private data. We call these pre-trained neural models neural intellectual properties (IP). Neural IPs raise multiple security concerns. On the one hand, as the IP user does not know about the training process, it is crucial to ensure the integrity of the neural IP. To this end, we investigate possible hidden malicious functionality, i.e. neural Trojans, that can be embedded into neural IPs and propose effective mitigation techniques. On the other hand, the neural IP owner may want to protect the NN model from reverse engineering attacks. However, it has been shown that hardware side-channels can be exploited to decipher the structure of neural networks. We propose both a novel attack approach based on cache timing side-channel and a defensive memory access mechanism.
NNs also raise challenges to conventional hardware security techniques. Specifically, we focus on its challenge to logic locking, a strong key-based protection of hardware IP against untrusted foundries by injecting incorrect behavior into the digital functionality when the key is incorrect. We formally prove a trade-off between the amount of injected error and the complexity of Boolean satisfiability (SAT)-based attacks to find the correct key. Due to the inherent error resiliency of NNs, state-of-the-art logic locking schemes fail to inject enough error to derail NN-based applications while maintaining exponential SAT complexity. To fix this issue, we propose a novel secure and effective logic locking scheme, called Strong Anti-SAT (SAS), to lock the hardware and make sure that the NN modes undergo significant accuracy loss when any wrong key is applied
Cofilin Activation in Peripheral CD4 T Cells of HIV-1 Infected Patients: A Pilot Study
Cofilin is an actin-depolymerizing factor that regulates actin dynamics critical for T cell migration and T cell activation. In unstimulated resting CD4 T cells, cofilin exists largely as a phosphorylated inactive form. Previously, we demonstrated that during HIV-1 infection of resting CD4 T cells, the viral envelope-CXCR4 signaling activates cofilin to overcome the static cortical actin restriction. In this pilot study, we have extended this in vitro observation and examined cofilin phosphorylation in resting CD4 T cells purified from the peripheral blood of HIV-1-infected patients. Here, we report that the resting T cells from infected patients carry significantly higher levels of active cofilin, suggesting that these resting cells have been primed in vivo in cofilin activity to facilitate HIV-1 infection. HIV-1-mediated aberrant activation of cofilin may also lead to abnormalities in T cell migration and activation that could contribute to viral pathogenesis.Department of Defense (National Defense Science and Engineering Fellowship); National Institute of Allergy and Infectious Diseases (AI069981
Logic Locking based Trojans: A Friend Turns Foe
Logic locking and hardware Trojans are two fields in hardware security that
have been mostly developed independently from each other. In this paper, we
identify the relationship between these two fields. We find that a common
structure that exists in many logic locking techniques has desirable properties
of hardware Trojans (HWT). We then construct a novel type of HWT, called
Trojans based on Logic Locking (TroLL), in a way that can evade
state-of-the-art ATPG-based HWT detection techniques. In an effort to detect
TroLL, we propose customization of existing state-of-the-art ATPG-based HWT
detection approaches as well as adapting the SAT-based attacks on logic locking
to HWT detection. In our experiments, we use random sampling as reference. It
is shown that the customized ATPG-based approaches are the best performing but
only offer limited improvement over random sampling. Moreover, their efficacy
also diminishes as TroLL's triggers become longer, i.e., have more bits
specified). We thereby highlight the need to find a scalable HWT detection
approach for TroLL.Comment: 9 pages, double column, 8 figures, IEEE forma
Measuring Hydrometeors with a Precipitation Microphysical Characteristics Sensor: Calibration and Field Measurements
Aiming at the simultaneous measurement of the size, shape, and fall velocity of precipitation particles in the natural environment, we present here a new ground-based precipitation microphysical characteristics sensor (PMCS) based on the particle imaging velocimetry technology. The PMCS can capture autocorrelated images of precipitation particles by double-exposure in one frame, by which the size, axis ratio, and fall velocity of precipitation particles can be calculated. The PMCS is calibrated by a series of glass balls with certain diameters under varying light conditions, and a self-adaptive threshold method is proposed. The shape, axis ratio, and fall velocity of raindrops were calculated and discussed based on the field measurement results of PMCS. The typical shape of large raindrop is an oblate ellipsoid, the axis ratio of raindrops decreases linearly with the diameter, the fall velocity of raindrops approaches its asymptote, and the above observed results are in good agreement with the empirical models; the synchronous observation of a PMCS, an OTT PARSIVEL disdrometer, and a rain gauge shows that the PMCS is able to measure the rain intensity, accumulated rainfall, and drop size distribution with high accuracy. These results have validated the performance of PMCS
Visual Confusion Label Tree For Image Classification
Convolution neural network models are widely used in image classification
tasks. However, the running time of such models is so long that it is not the
conforming to the strict real-time requirement of mobile devices. In order to
optimize models and meet the requirement mentioned above, we propose a method
that replaces the fully-connected layers of convolution neural network models
with a tree classifier. Specifically, we construct a Visual Confusion Label
Tree based on the output of the convolution neural network models, and use a
multi-kernel SVM plus classifier with hierarchical constraints to train the
tree classifier. Focusing on those confusion subsets instead of the entire set
of categories makes the tree classifier more discriminative and the replacement
of the fully-connected layers reduces the original running time. Experiments
show that our tree classifier obtains a significant improvement over the
state-of-the-art tree classifier by 4.3% and 2.4% in terms of top-1 accuracy on
CIFAR-100 and ImageNet datasets respectively. Additionally, our method achieves
124x and 115x speedup ratio compared with fully-connected layers on AlexNet and
VGG16 without accuracy decline.Comment: 9 pages, 5 figures, conferenc
A novel hybrid firefly algorithm for global optimization
Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate
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