Inyoung Kim

A Robust Physical Unclonable Function With Enhanced Challenge-Response Set

A Physical Unclonable Function (PUF) is a promising solution to many security issues due its ability to generate a die unique identifier that can resist cloning attempts as well as physical tampering. However, the efficiency of a PUF depends on its implementation cost, its reliability, its resiliency to attacks, and the amount of entropy in it. PUF entropy is used to construct crypto graphic keys, chip identifiers, or challenge-response pairs (CRPs) in a chip authentication mechanism. The amount of entropy in a PUF is limited by the circuit resources available to build a PUF. As a result, generating longer keys or larger sets of CRPs may increase PUF circuit cost. We address this limitation in a PUF by proposing an identity-mapping function that expands the set of CRPs of a ring-oscillator PUF (RO-PUF) with low area cost. The CRPs generated through this function exhibit strong PUF qualities in terms of uniqueness and reliability. To introduce the identity-mapping function, we formulate a novel PUF system model that uncouples PUF measurement from PUF identifier formation. We show the enhanced CRP generation capability of the new function using a statistical hypothesis test. An implementation of our technique on a low-cost FPGA platform shows at least 2 times savings in area compared to the traditional RO-PUF. The proposed technique is validated using a population of 125 chips, and its reliability over varying environmental conditions is shown.

Effect of Dietary Corn Oil Supplementation on Equine Gastric Fluid Acid, Sodium, and Prostaglandin E2 Content before and during Pentagastrin Infusion

The effect of corn oil (approximately 60% [wt/vol] linoleic acid) dietary supplementation on various components of equine gastric secretion was studied by use of a repeated-measures experimental design. Four healthy adult ponies were surgically fitted with gastric cannulas. The ponies were then fed a free-choice hay diet for 5 weeks, which was followed by 5 weeks of the same diet supplemented with 45 mL of corn oil daily. Gastric contents were analyzed under basal and pentagastrin-stimulated conditions once weekly during the latter 2 weeks on each diet. Gastric contents were collected at 30-minute intervals, and volume, hydrogen ion concentration, sodium content, and prostaglandin E2 (PGE2) content were measured. Data were analyzed by a linear fixed-effect modeling procedure. During the diet supplemented with corn oil, the ponies had, under basal and pentagastrin-stimulated conditions, significantly decreased acid output and significantly increased PGE2 and sodium outputs compared to those measured before corn oil supplementation. We conclude that corn oil supplementation may be an effective and inexpensive way to increase the protective properties of equine glandular gastric mucosa. This could be particularly helpful in reducing the chances of ulceration associated with nonsteroidal anti-inflammatory drug (NSAID) administration.

Meal size and starch content affect gastric emptying in horses

REASONS FOR PERFORMING STUDY Feeding practices have been associated with colic in horses. If meal size and composition have an effect on gastric emptying, this could be one of the mechanisms by which feeding practices are related to the occurrence of colic. OBJECTIVES To evaluate the effect of meal size and starch content on solid phase gastric emptying. METHODS Solid phase gastric emptying of 3 different radiolabelled meals, small low-starch (SmLS), small high-starch (SmHS) and large high-starch (LgHS) meals, was measured in 5 horses by scintigraphy using 99mTc-disofenin. Data were compared among meals using nonlinear mixed-effects models and paired t tests. RESULTS On a percentage basis, SmHS emptied significantly faster than LgHS and SmLS emptied significantly faster than SmHS meals. However, when meals of unequal size were compared by emptying rate in g/min and Kcal/min, LgHS emptied significantly faster than SmHS. CONCLUSIONS Meal size and composition affect gastric emptying. POTENTIAL RELEVANCE Further work needs to be performed in order to substantiate the possibility of a relationship between digestive functions and occurrence of colic and gastric ulcers.

Protein Interaction Predictions from Diverse Sources

Protein-protein interactions play an important role in many cellular processes. The availability of a comprehensive and accurate list of protein interactions can facilitate drug target discovery. Recent advances in high-throughput experimental technologies have generated enormous amounts of data and provided valuable resources for studying protein interactions. However, these technologies suffer from high error rates because of their inherent limitations. Therefore, computational approaches capable of incorporating multiple data sources are needed to fully take advantage of the rapid accumulation of data. In this review, we focus on the computational methods that integrate multiple data sources by combining direct measurements on protein interactions from diverse organisms, and by integrating different types of indirect information from various genomic and proteomic approaches.

From Statistics to Circuits: Foundations for Future Physical Unclonable Functions

Identity is an essential ingredient in secure protocols. Indeed, if we can no longer distinguish Alice from Bob, there is no point in doing a key exchange or in verifying their signatures. A human Alice and a human Bob identify one another based on looks, voice, or gestures.

Bayesian semiparametric regression models for evaluating pathway effects on continuous and binary clinical outcomes

Many statistical methods for microarray data analysis consider one gene at a time, and they may miss subtle changes at the single gene level. This limitation may be overcome by considering a set of genes simultaneously where the gene sets are derived from prior biological knowledge. Limited work has been carried out in the regression setting to study the effects of clinical covariates and expression levels of genes in a pathway either on a continuous or on a binary clinical outcome. Hence, we propose a Bayesian approach for identifying pathways related to both types of outcomes. We compare our Bayesian approaches with a likelihood-based approach that was developed by relating a least squares kernel machine for nonparametric pathway effect with a restricted maximum likelihood for variance components. Unlike the likelihood-based approach, the Bayesian approach allows us to directly estimate all parameters and pathway effects. It can incorporate prior knowledge into Bayesian hierarchical model formulation and makes inference by using the posterior samples without asymptotic theory. We consider several kernels (Gaussian, polynomial, and neural network kernels) to characterize gene expression effects in a pathway on clinical outcomes. Our simulation results suggest that the Bayesian approach has more accurate coverage probability than the likelihood-based approach, and this is especially so when the sample size is small compared with the number of genes being studied in a pathway. We demonstrate the usefulness of our approaches through its applications to a type II diabetes mellitus data set. Our approaches can also be applied to other settings where a large number of strongly correlated predictors are present.

A latent class modeling approach to detect network intrusion

This study presents a latent class modeling approach to examine network traffic data when labeled abnormal events are absent in training data, or such events are insufficient to fit a conventional regression model. Using six anomaly-associated risk factors identified from previous studies, the latent class model based on an unlabeled sample yielded acceptable classification results compared with a logistic regression model based on a labeled sample (correctly classified: 0.95 vs. 0.98, sensitivity: 0.99 vs. 0.99, and specificity: 0.77 vs. 0.97). The study demonstrates a great potency for using the latent class modeling technique to analyze network traffic data.

The Promise of Neurotechnology in Clinical Translational Science

Neurotechnology is broadly defined as a set of devices used to understand neural processes and applications that can potentially facilitate the brain’s ability to repair itself. In the past decade, an increasingly explicit understanding of basic biological mechanisms of brain-related illnesses has produced applications that allow a direct yet noninvasive method to index and manipulate the functioning of the human nervous system. Clinical scientists are poised to apply this technology to assess, treat, and better understand complex socioemotional processes that underlie many forms of psychopathology. In this review, we describe the potential benefits and hurdles, both technical and methodological, of neurotechnology in the context of clinical dysfunction. We also offer a framework for developing and evaluating neurotechnologies that is intended to expedite progress at the nexus of clinical science and neural-interface designs by providing a comprehensive vocabulary to describe the necessary features of neurotechnology in the clinic.

Profiling User Behavior for Intrusion Detection Using Item Response Modeling

Abstract Item response theory (IRT) is a modern test measurement theory that has been widely used in many research areas over the last decade. This paper presents an IRT modeling approach that fits network traffic to a “test” (normal or abnormal) model and estimates an expected test score of being anomaly-free to profile user behavior. With four anomaly-free associated variables identified from previous studies, the findings demonstrate that there is a remarkable difference in item characteristic curves between the user behavior patterns with anomalies and those that are anomaly-free, and such a difference can be quantitatively measured with the expected test score ranging from 0 to 100 where a high score is more likely to be associate with an anomaly-free pattern. More specifically, there are approximately 25 (SD = 4.0) points’ differences between a pattern with anomalies and one without. Our study demonstrates the potential feasibility and achievability of applying IRT for modern network security.

Semiparametric and nonparametric modeling for effect modification in matched studies

Abstract This study describes a new graphical method for assessing and characterizing effect modification by a matching covariate in matched case–control studies. This method to understand effect modification is based on a semiparametric model using a varying coefficient model. The method allows for nonparametric relationships between effect modification and other covariates, or can be useful in suggesting parametric models. This method can be applied to examining effect modification by any ordered categorical or continuous covariates for which cases have been matched with controls. The method applies to effect modification when causality might be reasonably assumed. An example from veterinary medicine is used to demonstrate our approach. The simulation results show that this method, when based on linear, quadratic and nonparametric effect modification, can be more powerful than both a parametric multiplicative model fit and a fully nonparametric generalized additive model fit.