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Dependence among cyber risks has been an essential and challenging component of risk management. The current study characterizes cyber dependence from both qualitative and quantitative perspectives based on L-hop propagation model. From the qualitative side, it is shown that cyber risks always possess positive association based on the proposed risk propagation model. From the quantitative side, an explicit formula for computing the fundamental dependence measure of covariance is provided for an arbitrary network. In particular, we study the impacts of factors—especially external and internal compromise probabilities, propagation depth, and network topologies—on dependence among cyber risks. We conclude by presenting some examples and applications.

The computation of the signature of a complex system is often challenging, as it may involve a large number of components and a complex architecture. In this study, we propose a novel approach to computing the signature of a system consisting of subsystems with shared components. The proposed approach relies on a new concept called decomposed survival signatures. This new concept can be efficiently used to compute the signature of a modular system with an arbitrary organizing structure and to provide explicit formulas for computation. Applications in cyber systems, transportation systems, and branched oil pipeline systems are highlighted.

Due to the increasing reliance on networks, defending a cyber system is of vital importance. In this paper, we consider an important mechanism of early warning for defending a cyber system that has become a key component of constructing network defense in practice. We study the reliability of a system under attack from single or multiple sources. In particular, we discuss the effect of an early warning mechanism on the system reliability. We then propose the optimal strategy for defending a cyber system with early warning components in the worst attack scenario. The theoretical results are further validated by simulation evidence.

The online storage of sensitive data enjoys many benefits such as flexibility, cost-savings, scalability, and convenience but it also poses a big concern on the data confidentiality and availability. To ensure the confidentiality and availability of sensitive data over a network system, the data partition technique is often employed. We study the optimal data partition strategy over an arbitrary network under cyber threats. Both the outside attack and the risk propagation (i.e., inside attack) are considered for the data partition. The data breach probability and retrieve probability are discussed under both limited and unlimited risk propagation for various scenarios. It is discovered that the risk propagation can have much more impact on the optimal partition strategy than that of outside attacks, and the unlimited risk propagation leads to more severer cyber risk. The network topology significantly impacts the partition strategy which hints that the network topology should never be overlooked in practice. The corruption due to compromise can lead to different partition strategies. An optimal partition model is developed for determining the optimal strategy and the pareto non-dominated solutions are recommended for practical use.

Phasor measurement units (PMUs) have been integrated into the smart grid for monitoring the operational state of system and improving the reliability. Due to the high cost of PMU installment, the optimal placement strategies have attracted considerable attention in the literature. However, the impacts of cyber threats on the placement have been largely ignored owing to the cyber complexities. This paper initializes the study on the optimal PMU placement in a smart grid under the cyber threats. A probabilistic model is developed for assessing the unobservable risk of the power grid. We characterize the impacts of several cyber factors on the PMU placements including the number of directly attacked PMUs, the dependence among attack outcomes, and risk propagation. We further study the impacts of cyber attacks on the allocation strategies under a bi-level placement model. In particular, a novel ‘greedy’ algorithm for PMU placement is introduced with the presence of cyber risks. Our studies show that the cyber risk can significantly increase the unobservability risk of a power system which in turn requires additional PMU allocations, and the dependence among cyber attacks can lead to more unobservable risk.

In this paper we investigate a cybersecurity model: An attacker can launch multiple attacks against a target with a termination strategy that says that the attacker will stop after observing a number of successful attacks or when the attacker is out of attack resources. However, the attacker's observation of the attack outcomes (i.e., random variables indicating whether the target is compromised or not) has an observation error that is specified by both a false-negative and a false-positive probability. The novelty of the model we study is the accommodation of the dependence between the attack outcomes, because the dependence was assumed away in the literature. In this model, we characterize the monotonicity and bounds of the compromise probability (i.e., the probability that the target is compromised). In addition to extensively showing the impact of dependence on quantities such as compromise probability and attack cost, we give methods for finding the optimal strategy that leads to maximum compromise probability or minimum attack cost. This study highlights that the dependence between random variables cannot be assumed away, because the results will be misleading.

The PMU network has been utilized to monitor the system operation of a smart grid in recent decades. This brings a significant cyber risk for the power grid as the attacker can manipulate the PMU network to introduce the false positive and false negative observation errors. A novel risk model is proposed for the PMU networks by considering the observation errors. It is discovered that ignoring the due observation errors can severely underestimate the PMU risks as shown by the theoretical and simulation studies. The risk mitigation strategies for improving the safety levels of PMUs with the observation errors are studied. It is found that the optimal mitigation strategy can be different when the observation errors are present. Numerical examples are presented for illustrations as well.

This paper studies capital allocation problems with the aggregate risk exceeding a certain threshold. We propose a novel capital allocation rule based on the Tail Mean–Variance principle. General formulas for the optimal capital allocations are proposed. Explicit formulas for optimal capital allocations are derived for multivariate elliptical distributions. Moreover, we give asymptotic allocation formulas for multivariate regular variation variables. Various numerical examples are given to illustrate the results, and real insurance data is discussed as well.

Vaccinium bracteatum Thunb. (VBT) is a traditional Chinese herbal medicine. The anti-diabetic activity of VBT leaves' polysaccharide (VBTLP) is studied in this paper. The results indicated VBTLP had a dose-dependent decrease on the blood glucose (BG) level, and the time effect of VBTLP on BG level was also significant. The insulin level of high dose group (HDG) was significantly higher (p

In this paper, the sample range from a heterogeneous exponential sample is shown to be larger than that from a homogeneous exponential sample in the sense of the star ordering. Then, by using this result, some equivalent characterizations of stochastic comparisons of sample ranges with respect to various stochastic orders are established. In this process, two open problems mentioned in Mao and Hu (2010) [16] are solved. The main results established here extend and strengthen several known results in the literature including those of Khaledi and Kochar (2000) [8] , Zhao and Li (2009) [22] and Genest et al. (2009) [7] .

This paper studies capital allocation problems using a general loss function. Stochastic comparisons are conducted for general loss functions in several scenarios: independent and identically distributed risks; independent but non-identically distributed risks; comonotonic risks. Applications in optimal capital allocations and policy limits allocations are discussed as well.

Two sufficient conditions for comparing convolutions of heterogeneous gamma random variables in terms of star order are established. It is further shown that if the scale parameters of heterogeneous gamma random variables are more dispersed in the sense of majorization, then the convolutions are more dispersed according to the right spread order, which generalizes and strengthens the results in Diaconis and Perlman (1987), Kochar and Xu (2010) and Zhao and Balakrishnan (2009).

A sufficient condition for comparing convolutions of heterogeneous exponential random variables in terms of right spread order is established. As a consequence, it is shown that a convolution of heterogeneous independent exponential random variables is more skewed than that of homogeneous exponential random variables in the sense of NBUE order. This gives a new insight into the distribution theory of convolutions of independent random variables. A sufficient condition is also derived for comparing such convolutions in terms of Lorenz order.

Let R"n be the range of a random sample X"1,...,X"n of exponential random variables with hazard rate @l. Let S"n be the range of another collection Y"1,...,Y"n of mutually independent exponential random variables with hazard rates @l"1,...,@l"n whose average is @l. Finally, let r and s denote the reversed hazard rates of R"n and S"n, respectively. It is shown here that the mapping [email protected]?s(t)/r(t) is increasing on (0,~) and that as a result, R"n=X"("n")-X"("1") is smaller than S"n=Y"("n")-Y"("1") in the likelihood ratio ordering as well as in the dispersive ordering. As a further consequence of this fact, X"("n") is seen to be more stochastically increasing in X"("1") than Y"("n") is in Y"("1"). In other words, the pair (X"("1"),X"("n")) is more dependent than the pair (Y"("1"),Y"("n")) in the monotone regression dependence ordering. The latter finding extends readily to the more general context where X"1,...,X"n form a random sample from a continuous distribution while Y"1,...,Y"n are mutually independent lifetimes with proportional hazard rates.

In this paper, we introduce a new copula-based dependence order to compare the relative degree of dependence between two pairs of random variables. Relationship of the new order to the existing dependence orders is investigated. In particular, the new ordering is stronger than the partial ordering, more monotone regression dependence as developed by Avérous et al. [J. Avérous, C. Genest, S.C. Kochar, On dependence structure of order statistics, Journal of Multivariate Analysis 94 (2005) 159–171]. Applications of this partial order to order statistics, k-record values and frailty models are given.

This note investigates the negative dependence in frailty models. First, we show that the frailty variable and the overall population variable are negatively likelihood ratio dependent and derive an upper bound for the survival function of the population with higher frailty. Secondly, we prove that the DFR property and the logconvex hazard rate of the baseline variable imply the DLR property of the population variable. Finally, we further prove that the likelihood ratio order, hazard rate order and reversed hazard rate order between two frailty variables imply the likelihood ratio order, reversed hazard rate order, and hazard rate order between the corresponding overall population variables, respectively.

In this note, we further study the properties of excess wealth (or right spread) order and the location independent riskier order. It is proved that if X is less variable than Y according to excess wealth order, then X n : n − X k : n ≤ icx Y n : n − Y k : n for k = 0 , 1 , … , n − 1 , where X 0 : n = Y 0 : n ≡ 0 . Similar results are obtained for location independent riskier order. An application in k -price business auction models is presented as well.

For two non-negative random variables X and Y, it is shown that if X is smaller than Y in the sense of the likelihood ratio order and either X or Y is of decreasing likelihood ratio, then the m-spacing V k : n ( m ) of an X-sample is smaller than W k : n ( m ) , the m-spacing of a Y-sample, in terms of the likelihood ratio order. It is also proved that a similar result for the upshifted likelihood ratio order is valid. Finally, it is proved that the decreasing failure rate in average property of a population X is preserved by sample spacing V k : n ( 1 ) .