Security Challenges in Smart-Grid Metering and Control Systems

The smart grid is a next-generation power system that is increasingly attracting the attention of government, industry, and academia. It is an upgraded electricity network that depends on two-way digital communications between supplier and consumer that in turn give support to intelligent metering and monitoring systems. Considering that energy utilities play an increasingly important role in our daily life, smart-grid technology introduces new security challenges that must be addressed. Deploying a smart grid without adequate security might result in serious consequences such as grid instability, utility fraud, and loss of user information and energy-consumption data. Due to the heterogeneous communication architecture of smart grids, it is quite a challenge to design sophisticated and robust security mechanisms that can be easily deployed to protect communications among different layers of the smart grid-infrastructure. In this article, we focus on the communication-security aspect of a smart-grid metering and control system from the perspective of cryptographic techniques, and we discuss different mechanisms to enhance cybersecurity of the emerging smart grid. We aim to provide a comprehensive vulnerability analysis as well as novel insights on the cybersecurity of a smart grid

An Integrated Analysis of Federal Milk Marketing Order Price Differential Policy and Climate Change Effects in Dairy Industry

This dissertation investigates the match of current Federal Milk Marketing Order (FMMO) price differential policy with current conditions and the effects of shifting diesel prices on price differentials along with climate effects on milk production. A spatial mathematical model (MilkOrdIII) of the dairy industry is modified to include milk components and organic milk then used to study milk pricing, and the effects of changing diesel prices. Subsequently, climate impacts are explored using spatial econometric panel modeling. This work is reported from three aspects. Firstly, it addresses how the present Class I price differentials under FMMOs compare to an model-generated idealized set. It also does a comparison for Class II, III, and IV milk and constructs price surfaces for organic milk, and milk components. The results show that the current Class I price differentials are not a good reflection of ideal differentials under today's conditions. We find the price differential pattern is similar to that of today's policy but the range of values is substantially wider. Thus updating may be desirable. For organic, Class II and IV milk and butterfat may merit regionally differentiated prices. Class III and the other milk components appear to be handled with a uniform price. Secondly, this research examines the diesel price influences on price differentials and the possibility of updating the differentials as diesel prices change. The results show price differentials are significantly affected by diesel price with higher prices increasing the differentials for raw milk and protein; decreasing those for butterfat and other solids non-fat. Formulas for updating differentials as the diesel price shifts are developed based on econometric estimation over the ideal price differential results. Thirdly, the climate change analysis examines climate impacts on milk production then estimates the impact of projected climate change. The results show heat stress index, exerts an inverse U-shaped relationship with a threshold of 72; under an RCP 8.5 climatic projection for 2030 milk production would increase in the West and Northeast and decline in the Mid-west and South. Also using spatial econometric panel models improves model fit likely due to reduced biases from spatially correlated omitted variables

Efficient Cryptographic Algorithms and Protocols for Mobile Ad Hoc Networks

As the next evolutionary step in digital communication systems, mobile ad hoc networks (MANETs) and their specialization like wireless sensor networks (WSNs) have been attracting much interest in both research and industry communities. In MANETs, network nodes can come together and form a network without depending on any pre-existing infrastructure and human intervention. Unfortunately, the salient characteristics of MANETs, in particular the absence of infrastructure and the constrained resources of mobile devices, present enormous challenges when designing security mechanisms in this environment. Without necessary measures, wireless communications are easy to be intercepted and activities of users can be easily traced. This thesis presents our solutions for two important aspects of securing MANETs, namely efficient key management protocols and fast implementations of cryptographic primitives on constrained devices. Due to the tight cost and constrained resources of high-volume mobile devices used in MANETs, it is desirable to employ lightweight and specialized cryptographic primitives for many security applications. Motivated by the design of the well-known Enigma machine, we present a novel ultra-lightweight cryptographic algorithm, referred to as Hummingbird, for resource-constrained devices. Hummingbird can provide the designed security with small block size and is resistant to the most common attacks such as linear and differential cryptanalysis. Furthermore, we also present efficient software implementations of Hummingbird on 4-, 8- and 16-bit microcontrollers from Atmel and Texas Instruments as well as efficient hardware implementations on the low-cost field programmable gate arrays (FPGAs) from Xilinx, respectively. Our experimental results show that after a system initialization phase Hummingbird can achieve up to 147 and 4.7 times faster throughput for a size-optimized and a speed-optimized software implementation, respectively, when compared to the state-of-the-art ultra-lightweight block cipher PRESENT on the similar platforms. In addition, the speed optimized Hummingbird encryption core can achieve a throughput of 160.4 Mbps and the area optimized encryption core only occupies 253 slices on a Spartan-3 XC3S200 FPGA device. Bilinear pairings on the Jacobians of (hyper-)elliptic curves have received considerable attention as a building block for constructing cryptographic schemes in MANETs with new and novel properties. Motivated by the work of Scott, we investigate how to use efficiently computable automorphisms to speed up pairing computations on two families of non-supersingular genus 2 hyperelliptic curves over prime fields. Our findings lead to new variants of Miller's algorithm in which the length of the main loop can be up to 4 times shorter than that of the original Miller's algorithm in the best case. We also generalize Chatterjee et al.'s idea of encapsulating the computation of the line function with the group operations to genus 2 hyperelliptic curves, and derive new explicit formulae for the group operations in projective and new coordinates in the context of pairing computations. Efficient software implementation of computing the Tate pairing on both a supersingular and a non-supersingular genus 2 curve with the same embedding degree of k = 4 is investigated. Combining the new algorithm with known optimization techniques, we show that pairing computations on non-supersingular genus 2 curves over prime fields use up to 55.8% fewer field operations and run about 10% faster than supersingular genus 2 curves for the same security level. As an important part of a key management mechanism, efficient key revocation protocol, which revokes the cryptographic keys of malicious nodes and isolates them from the network, is crucial for the security and robustness of MANETs. We propose a novel self-organized key revocation scheme for MANETs based on the Dirichlet multinomial model and identity-based cryptography. Firmly rooted in statistics, our key revocation scheme provides a theoretically sound basis for nodes analyzing and predicting peers' behavior based on their own observations and other nodes' reports. Considering the difference of malicious behaviors, we proposed to classify the nodes' behavior into three categories, namely good behavior, suspicious behavior and malicious behavior. Each node in the network keeps track of three categories of behavior and updates its knowledge about other nodes' behavior with 3-dimension Dirichlet distribution. Based on its own analysis, each node is able to protect itself from malicious attacks by either revoking the keys of the nodes with malicious behavior or ceasing the communication with the nodes showing suspicious behavior for some time. The attack-resistant properties of the resulting scheme against false accusation attacks launched by independent and collusive adversaries are also analyzed through extensive simulations. In WSNs, broadcast authentication is a crucial security mechanism that allows a multitude of legitimate users to join in and disseminate messages into the networks in a dynamic and authenticated way. During the past few years, several public-key based multi-user broadcast authentication schemes have been proposed in the literature to achieve immediate authentication and to address the security vulnerability intrinsic to μTESLA-like schemes. Unfortunately, the relatively slow signature verification in signature-based broadcast authentication has also incurred a series of problems such as high energy consumption and long verification delay. We propose an efficient technique to accelerate the signature verification in WSNs through the cooperation among sensor nodes. By allowing some sensor nodes to release the intermediate computation results to their neighbors during the signature verification, a large number of sensor nodes can accelerate their signature verification process significantly. When applying our faster signature verification technique to the broadcast authentication in a 4×4 grid-based WSN, a quantitative performance analysis shows that our scheme needs 17.7%~34.5% less energy and runs about 50% faster than the traditional signature verification method

Hepatic and intestinal biotransformation gene expression and drug disposition in a dextran sulfate sodium-induced colitis mouse model

We examined the impact of gut inflammation on the expression of cytochrome P450 (P450) and other biotransformation genes in male mice using a dextran sulfate sodium (DSS)-induced colitis model. Several P450 isoforms, including CYP1A, CYP2B, CYP2C, and CYP3A, were down-regulated, accompanied by decreases in microsomal metabolism of diclofenac and nifedipine, in the liver and small intestine. The impact of the colitis on in vivo clearance of oral drugs varied for four different drugs tested: a small decrease for nifedipine, a relatively large decrease for lovastatin, but no change for pravastatin, and a large decrease in the absorption of cyclosporine A. To further assess the scope of influence of gut inflammation on gene expression, we performed genome-wide expression analysis using RNA-seq, which showed down-regulation of many CYPs, non-CYP phase-I enzymes, phase-II enzymes and transporters, and up-regulation of many other members of these gene families, in both liver and intestine of adult C57BL/6 mice, by DSS-induced colitis. Overall, our results indicate that gut inflammation suppresses the expression of many P450s and other biotransformation genes in the intestine and liver, and alters the pharmacokinetics for some but not all drugs, potentially affecting therapeutic efficacy or causing adverse effects in a drug-specific fashion.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Contrastive Prompt Learning-based Code Search based on Interaction Matrix

Code search aims to retrieve the code snippet that highly matches the given query described in natural language. Recently, many code pre-training approaches have demonstrated impressive performance on code search. However, existing code search methods still suffer from two performance constraints: inadequate semantic representation and the semantic gap between natural language (NL) and programming language (PL). In this paper, we propose CPLCS, a contrastive prompt learning-based code search method based on the cross-modal interaction mechanism. CPLCS comprises:(1) PL-NL contrastive learning, which learns the semantic matching relationship between PL and NL representations; (2) a prompt learning design for a dual-encoder structure that can alleviate the problem of inadequate semantic representation; (3) a cross-modal interaction mechanism to enhance the fine-grained mapping between NL and PL. We conduct extensive experiments to evaluate the effectiveness of our approach on a real-world dataset across six programming languages. The experiment results demonstrate the efficacy of our approach in improving semantic representation quality and mapping ability between PL and NL

Inversion-Free Arithmetic on Genus 3 Hyperelliptic Curves

Hyperelliptic curve cryptosystem (HECC) is becoming more and more promising for network security applications because of the common effort of several academic and industrial organizations. With short operand size compared to other public key cryptosystems, HECC has showed excellent performance in embedded processors. Recently years, many effort has been made to investigate all kinds of explicit formulae for speeding up group operation of HECC. In this paper, explicit formulae without using inversion for genus 3 HECC are given. We introduce a further coordinate to collect the common denominator of the usual 6 coordinates. The proposed formulae can be used in smart card where inversion is much more expensive than multiplication