Allocating security resources to a water supply network

This paper develops a method for allocating a security budget to a water supply network so as to maximize the network's resilience to physical attack. The method integrates max-min linear programming, hydraulic simulation, and genetic algorithms for constraint generation. The objective is to find a security allocation that maximizes an attacker's marginal cost of inflicting damage through the destruction of network components. We illustrate the method on two example networks, one large and one small, and investigate its allocation effectiveness and computational characteristics.     

An adaptive transmission mechanism for parallel applications in wireless environments

Abstract

Parallel computing is a common technique for reducing execution time in distributed systems. An application is divided into several subtasks that can be executed simultaneously on a set of computers. Numerous parallel computing protocols and experiments have been measured on wired network environments, but little attention has been devoted to wireless networks. Wireless environments own characteristics that are unusual in wired networks, such as limited bandwidth, frequent disconnection, low power, and mobility. Due to the unique characteristics, the performance of parallel computing in wireless networks is degraded. This paper implements an adaptive transmission mechanism to cope with network contention and frequent disconnection. Experimental results show that the mechanism reduced total execution time effectively.     

DHT系统的安全性优化方法研究

对分布式哈希表(DHT)系统的安全脆弱性问题进行了研究,提出了多种安全性优化策略,并给出了一个原型系统。进行了真实网络实验,实验数据表明,现有DHT网络易受索引毒害和路由污染攻击,产生的错误查询结果甚至会引发更大规模的网络安全事件。通过改进一个个DHT系统的节点ID生成机制、路由表更新机制和搜索路径选择机制,从系统运行的各个阶段提升其安全场,抵御攻击者共谋。基于上述方法设计的原型系统在保证平均查询跳数增加不到1跳的情况下,在共谋攻击节点占比60%的网络中,将系统查询成功率保持在65%以上,其方法适用于各种分布式哈希表结构,具有重要的实际应用前景。     

Power System Resiliency and Wide Area Control Employing Deep Learning Algorithm

The power transfer capability of the smart transmission grid-connected networks needs to be reduced by inter-area oscillations. Due to the fact that inter-area modes of oscillations detain and make instability of power transmission networks. This fact is more noticeable in smart grid-connected systems. The smart grid infrastructure has more renewable energy resources installed for its operation. To overcome this problem, a deep learning wide-area controller is proposed for real-time parameter control and smart power grid resilience on oscillations inter-area modes. The proposed Deep Wide Area Controller (DWAC) uses the Deep Belief Network (DBN). The network weights are updated based on real-time data from Phasor measurement units. Resilience assessment based on failure probability, financial impact, and time-series data in grid failure management determine the norm H₂. To demonstrate the effectiveness of the proposed framework, a time-domain simulation case study based on the IEEE-39 bus system was performed. For a one-channel attack on the test system, the resiliency index increased to 0.962, and inter-area damping ξ was reduced to 0.005. The obtained results validate the proposed deep learning algorithm’s efficiency on damping inter-area and local oscillation on the 2-channel attack as well. Results also offer robust management of power system resilience and timely control of the operating conditions.     

On tandem stochastic networks with time-deteriorating product quality

We consider an n-site tandem stochastic production network where each product moves sequentially through the sites, and the product's quality deteriorates with its sojourn time in the system. At each site the product goes through two stages: the first stage is a processing operation with a generally-distributed random duration. This operation either does or does not conclude successfully; in the latter case, the operation is repeated immediately. Once the processing operation concludes successfully, the product goes through an inspection stage lasting a generally-distributed random duration. At the end of the inspection the product's state is determined as follows: either (i) it requires additional processing and moves forward to the next site; or (ii) it is found ‘good’ and exits the network with quality value depending on its total sojourn time in the system; or (iii) it is declared ‘failed’, discarded, and exits the network with zero quality value. Two scenarios are analysed: (i) a new product enters the system only after the preceding product has exited and (ii) the network is a tandem Jackson-type system. For each scenario, we construct both time-dependent and quality-dependent performance measures. In the case where the sites can be arranged in an arbitrary order, we derive easy to implement optimal index-type policies of ordering the sites so as to maximise the quality rate of the production network.     

Evaluating the damage associated with intentional network disintegration

This paper presents a method for evaluating an expected damage associated with disintegrating complex networks with a given topology into isolated sub-networks (clusters) as a result of intentional attack on randomly chosen network links. The method is based on a multi-dimensional spectra approach for evaluating the probability of network disintegration into a given number of sub-networks when a fixed number of randomly chosen links is eliminated. It also uses the contest success function that evaluates destruction probability of individual links as a function of per-link attack and defense efforts. It is assumed that the defender has no information about the attacker's actions and the attacker has no information about the network structure. The method allows the analysts to compare different network topologies and to choose one with the minimal expected damage under conditions of uncertainty. Illustrative examples are presented.     

Adversarial Attacks on License Plate Recognition Systems

The license plate recognition system (LPRS) has been widely adopted in daily life due to its efficiency and high accuracy. Deep neural networks are commonly used in the LPRS to improve the recognition accuracy. However, researchers have found that deep neural networks have their own security problems that may lead to unexpected results. Specifically, they can be easily attacked by the adversarial examples that are generated by adding small perturbations to the original images, resulting in incorrect license plate recognition. There are some classic methods to generate adversarial examples, but they cannot be adopted on LPRS directly. In this paper, we modify some classic methods to generate adversarial examples that could mislead the LPRS. We conduct extensive evaluations on the HyperLPR system and the results show that the system could be easily attacked by such adversarial examples. In addition, we show that the generated images could also attack the black-box systems; we show some examples that the Baidu LPR system also makes incorrect recognitions. We hope this paper could help improve the LPRS by realizing the existence of such adversarial attacks.     

Stabilization of periodic orbits near a subcritical Hopf bifurcation in delay-coupled networks

We study networks of delay-coupled oscillators with the aim to extend time-delayed feedback control to networks. We show that unstable periodic orbits of a network can be stabilized by a noninvasive, delayed coupling. We state criteria for stabilizing the orbits by delay-coupling in networks and apply these to the case where the local dynamics is close to a subcritical Hopf bifurcation, which is representative of systems with torsion-free unstable periodic orbits. Using the multiple scale method and the master stability function approach, the network system is reduced to the normal form, and the characteristic equations for Floquet exponents are derived in an analytical form, which reveals the coupling parameters for successful stabilization. Finally, we illustrate the results by numerical simulations of the Lorenz system close to a subcritical Hopf bifurcation. The unstable periodic orbits in this system have no torsion, and hence cannot be stabilized by the conventional time delayed-feedback technique.     

Energy Optimised Security against Wormhole Attack in IoT-Based Wireless Sensor Networks

An IoT-based wireless sensor network (WSN) comprises many small sensors to collect the data and share it with the central repositories. These sensors are battery-driven and resource-restrained devices that consume most of the energy in sensing or collecting the data and transmitting it. During data sharing, security is an important concern in such networks as they are prone to many threats, of which the deadliest is the wormhole attack. These attacks are launched without acquiring the vital information of the network and they highly compromise the communication, security, and performance of the network. In the IoT-based network environment, its mitigation becomes more challenging because of the low resource availability in the sensing devices. We have performed an extensive literature study of the existing techniques against the wormhole attack and categorised them according to their methodology. The analysis of literature has motivated our research. In this paper, we developed the ESWI technique for detecting the wormhole attack while improving the performance and security. This algorithm has been designed to be simple and less complicated to avoid the overheads and the drainage of energy in its operation. The simulation results of our technique show competitive results for the detection rate and packet delivery ratio. It also gives an increased throughput, a decreased end-to-end delay, and a much-reduced consumption of energy.     

An entropic characterization of protein interaction networks and cellular robustness.

The structure of molecular networks is believed to determine important aspects of their cellular function, such as the organismal resilience against random perturbations. Ultimately, however, cellular behaviour is determined by the dynamical processes, which are constrained by network topology. The present work is based on a fundamental relation from dynamical systems theory, which states that the macroscopic resilience of a steady state is correlated with the uncertainty in the underlying microscopic processes, a property that can be measured by entropy. Here, we use recent network data from large-scale protein interaction screens to characterize the diversity of possible pathways in terms of network entropy. This measure has its origin in statistical mechanics and amounts to a global characterization of both structural and dynamical resilience in terms of microscopic elements. We demonstrate how this approach can be used to rank network elements according to their contribution to network entropy and also investigate how this suggested ranking reflects on the functional data provided by gene knockouts and RNAi experiments in yeast and Caenorhabditis elegans. Our analysis shows that knockouts of proteins with large contribution to network entropy are preferentially lethal. This observation is robust with respect to several possible errors and biases in the experimental data. It underscores the significance of entropy as a fundamental invariant of the dynamical system, and as a measure of structural and dynamical properties of networks. Our analytical approach goes beyond the phenomenological studies of cellular robustness based on local network observables, such as connectivity. One of its principal achievements is to provide a rationale to study proxies of cellular resilience and rank proteins according to their importance within the global network context.     

Assessing supply chain robustness to links failure

Supply chain networks need to respond efficiently to operation disruptions, as one of their aims is to guarantee the on time delivery of products. Hence, robustness has become one of the important issues to consider when designing supply networks. There are alternative ways to measure what robustness means in this context. In this paper, we propose a new metric based on the effect on service level of the collapse of active transportation links. Numerical experiments are carried out to understand how different design factors affect robustness. Robustness under a targeted attack is compared with robustness to random failures. Results show that flow complexity (i.e. the number of potential transportation links between supply network nodes) is the most influential factor affecting supply network and its robustness, as well as the service level that can be maintained after disruptions. Thus, diversification both in supply sources and transportation routes seems to be key to robustness.     

城市燃气管网三维度抗震韧性定量评估方法

为了量化评估城市燃气管网系统的抗震韧性,并充分考虑地震动输入,管网连通性能评估,以及修复过程三个方面的不确定性,该文提出了技术维度、组织维度、社会维度下的燃气管网抗震韧性定量评估流程.衔接地震动预测方程(GMPE)输入,基于蒙特卡罗模拟对燃气管网连通性进行计算.通过随机模拟修复资源分配求得燃气管网在每次模拟破坏工况下的...     

Cooling‐Triggered Shapeshifting Hydrogels with Multi‐Shape Memory Performance

Heating‐triggered shape actuation is vital for biomedical applications. The likely overheating and subsequent damage of surrounding tissue, however, severely limit its utilization in vivo. Herein, cooling‐triggered shapeshifting is achieved by designing dual‐network hydrogels that integrate a permanent network for elastic energy storage and a reversible network of hydrophobic crosslinks for “freezing” temporary shapes when heated. Upon cooling to 10 °C, the hydrophobic interactions weaken and allow recovery of the original shape, and thus programmable shape alterations. Further, multiple temporary shapes can be encoded independently at either different temperatures or different times during the isothermal network formation. The ability of these hydrogels to shapeshift at benign conditions may revolutionize biomedical implants and soft robotics.     

基于BP神经网络的RC框架结构地震易损性曲面分析:考虑地震动强度和持时的影响

与短持时地震动相比,长持时地震动会加剧结构的损伤,增加结构的失效概率,因此有必要更充分地研究地震动持时特性对结构地震易损性分析结果的影响。该文提出了一种基于BP神经网络的地震易损性曲面分析方法,使用神经网络模型,综合考虑地震动强度和持时特性对结构地震需求的影响,并进行地震易损性分析,得到不同损伤水平下考虑地震动持时特性的结构易损性曲面。选用3个不同高度的钢筋混凝土框架结构为研究对象,分别选择具有长、短持时特性的2组地震动记录为输入,采用BP神经网络模型建立地震动强度指标与结构响应间的关系,在此基础上得到目标地震易损性曲面,并对该方法的有效性进行讨论。分析结果表明,研究建立的BP神经网络模型精度较高,依据该方法可得到可信的损伤概率分析结果。相比于传统方法,神经网络可以更为有效和准确地建立持时与结构损伤的相关关系,得到考虑持时特性的易损性分析结果。该文的方法亦可进一步拓展,将更多地震动特性纳入地震易损性分析过程,具有明确的应用前景。     

Mitigating supply chain disruptions through interconnected logistics services in the Physical Internet

This paper investigates the resilience of inventory models using interconnected logistics services in the Physical Internet (PI). With traditional supply chain network design, companies define and optimise their own logistics networks, resulting in current logistics systems being a set of independent heterogeneous logistics networks. The concept of PI aims to integrate independent logistics networks into a global, open, interconnected system. Prior research has shown that new inventory models enabled by and applied to PI could help reduce inventory levels thanks to its high flexibility. Continuing along these lines, this paper examines how inventory models applying PI deal with disruptions at hubs and plants. To attain this, a single product inventory problem with uncertain demands and stochastic supply disruptions is studied. A simulation-based optimisation model is proposed to determine inventory control decisions. The results suggest that the PI inventory model, with greater agility and flexibility, outperforms the current classic inventory models in terms of resilience. Moreover, the difference in performance increases when the product value, penalty costs and disruption frequency increases. This paper indicates a novel approach to build a resilient supply network.     

Fostering emergent resilience: the complex adaptive supply network of disaster relief

The frequency and intensity of disasters continue to increase. Following large-scale and catastrophic disasters, local organisations integrate with other responding organisations to form hastily disaster relief supply chain networks. Such supply networks are infrequently activated in a single location, generate unparalleled uncertainty, change quickly, and are driven by the urgency of saving lives and restoring livelihoods. Unfortunately, even where sound supply chain management practices are used, supply networks have encountered diverse levels of resilience and adequate disaster relief performance has remained elusive. In this paper, several unique characteristics that disaster relief efforts exhibit are examined as compared with demand-driven, steady-state supply chains. Important differences in the flows of resource, money, and information are identified. A complex adaptive supply network (CASN) lens is used to frame what existing literature has uncovered regarding disaster relief efforts, showing how relief organisations, their interactions, and their environmental context help determine the level of resilience that supply networks experience following disasters. This CASN characterisation is leveraged to help explain why traditional supply chain management practices lead to varied results in disaster relief. Finally, complexity science theory is drawn on to set forth eight testable propositions that may help to enhance supply network resilience.     

Prophet_TD Routing Algorithm Based on Historical Throughput and Encounter Duration

Opportunistic networks are self-organizing networks that do not require a complete path between the source node and the destination node as it uses encounter opportunities brought by nodes movement to achieve network communication. Opportunistic networks routing algorithms are numerous and can be roughly divided into four categories based on different forwarding strategies. The Prophet routing algorithm is an important routing algorithm in opportunistic networks. It forwards messages based on the encounter probability between nodes, and has good innovation significance and optimization potential. However, the Prophet routing algorithm does not consider the impact of the historical throughput of the node on message transmission, nor does it consider the impact of the encounter duration between nodes on message transmission. Therefore, to improve the transmission efficiency of opportunistic networks, this paper based on the Prophet routing algorithm, fuses the impact of the historical throughput of the node and the encounter duration between nodes on message transmission at the same time, and proposes the Prophet_TD routing algorithm based on the historical throughput and the encounter duration. This paper uses the Opportunistic Networks Environment v1.6.0 (the ONE v1.6.0) as the simulation platform, controls the change of running time and the number of nodes respectively, conducts simulation experiments on the Prophet_TD routing algorithm. The simulation results show that compared to the traditional Prophet routing algorithm, on the whole, the Prophet_TD routing algorithm has a higher message delivery rate and a lower network overhead rate, and its average latency is also lower when node density is large.     

三峡三期下游围堰爆破拆除起爆网路设计与可靠性分析

三峡三期下游围堰爆破拆除网路运用了非电复式交叉接力起爆网路。本文介绍了该工程起爆网路的设计,定量分析了单起爆点起爆同双起爆点起爆及单式加强网路、复式加强网路同复式交叉网路起爆可靠度差异,并建立了数学计算模型,供同类工程借鉴。     

Edge Metric Dimension of Honeycomb and Hexagonal Networks for IoT

Wireless Sensor Network (WSN) is considered to be one of the fundamental technologies employed in the Internet of things (IoT); hence, enabling diverse applications for carrying out real-time observations. Robot navigation in such networks was the main motivation for the introduction of the concept of landmarks. A robot can identify its own location by sending signals to obtain the distances between itself and the landmarks. Considering networks to be a type of graph, this concept was redefined as metric dimension of a graph which is the minimum number of nodes needed to identify all the nodes of the graph. This idea was extended to the concept of edge metric dimension of a graph G, which is the minimum number of nodes needed in a graph to uniquely identify each edge of the network. Regular plane networks can be easily constructed by repeating regular polygons. This design is of extreme importance as it yields high overall performance; hence, it can be used in various networking and IoT domains. The honeycomb and the hexagonal networks are two such popular mesh-derived parallel networks. In this paper, it is proved that the minimum landmarks required for the honeycomb network HC(n), and the hexagonal network HX(n) are 3 and 6 respectively. The bounds for the landmarks required for the hex-derived network HDN1(n) are also proposed.     

Phase transitions and assortativity in models of gene regulatory networks evolved under different selection processes

We study a simplified model of gene regulatory network evolution in which links (regulatory interactions) are added via various selection rules that are based on the structural and dynamical features of the network nodes (genes). Similar to well-studied models of ‘explosive’ percolation, in our approach, links are selectively added so as to delay the transition to large-scale damage propagation, i.e. to make the network robust to small perturbations of gene states. We find that when selection depends only on structure, evolved networks are resistant to widespread damage propagation, even without knowledge of individual gene propensities for becoming ‘damaged’. We also observe that networks evolved to avoid damage propagation tend towards disassortativity (i.e. directed links preferentially connect high degree ‘source’ genes to low degree ‘target’ genes and vice versa). We compare our simulations to reconstructed gene regulatory networks for several different species, with genes and links added over evolutionary time, and we find a similar bias towards disassortativity in the reconstructed networks.