AIB-OR: Improving Onion Routing Circuit Construction Using Anonymous Identity-Based Cryptosystems

The rapid growth of Internet applications has made communication anonymity an increasingly important or even indispensable security requirement. Onion routing has been employed as an infrastructure for anonymous communication over a public network, which provides anonymous connections that are strongly resistant to both eavesdropping and traffic analysis. However, existing onion routing protocols usually exhibit poor performance due to repeated encryption operations. In this paper, we first present an improved anonymous multi-receiver identity-based encryption (AMRIBE) scheme, and an improved identity-based one-way anonymous key agreement (IBOWAKE) protocol. We then propose an efficient onion routing protocol named AIB-OR that provides provable security and strong anonymity. Our main approach is to use our improved AMRIBE scheme and improved IBOWAKE protocol in onion routing circuit construction. Compared with other onion routing protocols, AIB-OR provides high efficiency, scalability, strong anonymity and fault tolerance. Performance measurements from a prototype implementation show that our proposed AIB-OR can achieve high bandwidths and low latencies when deployed over the Internet.     

Multiplex Networks of Cortical and Hippocampal Neurons Revealed at Different Timescales

Recent studies have emphasized the importance of multiplex networks – interdependent networks with shared nodes and different types of connections – in systems primarily outside of neuroscience. Though the multiplex properties of networks are frequently not considered, most networks are actually multiplex networks and the multiplex specific features of networks can greatly affect network behavior (e.g. fault tolerance). Thus, the study of networks of neurons could potentially be greatly enhanced using a multiplex perspective. Given the wide range of temporally dependent rhythms and phenomena present in neural systems, we chose to examine multiplex networks of individual neurons with time scale dependent connections. To study these networks, we used transfer entropy – an information theoretic quantity that can be used to measure linear and nonlinear interactions – to systematically measure the connectivity between individual neurons at different time scales in cortical and hippocampal slice cultures. We recorded the spiking activity of almost 12,000 neurons across 60 tissue samples using a 512-electrode array with 60 micrometer inter-electrode spacing and 50 microsecond temporal resolution. To the best of our knowledge, this preparation and recording method represents a superior combination of number of recorded neurons and temporal and spatial recording resolutions to any currently available in vivo system. We found that highly connected neurons (“hubs”) were localized to certain time scales, which, we hypothesize, increases the fault tolerance of the network. Conversely, a large proportion of non-hub neurons were not localized to certain time scales. In addition, we found that long and short time scale connectivity was uncorrelated. Finally, we found that long time scale networks were significantly less modular and more disassortative than short time scale networks in both tissue types. As far as we are aware, this analysis represents the first systematic study of temporally dependent multiplex networks among individual neurons.     

Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks

Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains.     

On the Inference of Functional Circadian Networks Using Granger Causality

Being able to infer one way direct connections in an oscillatory network such as the suprachiastmatic nucleus (SCN) of the mammalian brain using time series data is difficult but crucial to understanding network dynamics. Although techniques have been developed for inferring networks from time series data, there have been no attempts to adapt these techniques to infer directional connections in oscillatory time series, while accurately distinguishing between direct and indirect connections. In this paper an adaptation of Granger Causality is proposed that allows for inference of circadian networks and oscillatory networks in general called Adaptive Frequency Granger Causality (AFGC). Additionally, an extension of this method is proposed to infer networks with large numbers of cells called LASSO AFGC. The method was validated using simulated data from several different networks. For the smaller networks the method was able to identify all one way direct connections without identifying connections that were not present. For larger networks of up to twenty cells the method shows excellent performance in identifying true and false connections; this is quantified by an area-under-the-curve (AUC) 96.88%. We note that this method like other Granger Causality-based methods, is based on the detection of high frequency signals propagating between cell traces. Thus it requires a relatively high sampling rate and a network that can propagate high frequency signals.     

Optimal Routing in General Finite Multi-Server Queueing Networks

The design of general finite multi-server queueing networks is a challenging problem that arises in many real-life situations, including computer networks, manufacturing systems, and telecommunication networks. In this paper, we examine the optimal routing problem in arbitrary configured acyclic queueing networks. The performance of the finite queueing network is evaluated with a known approximate performance evaluation method and the optimization is done by means of a heuristics based on the Powell algorithm. The proposed methodology is then applied to determine the optimal routing probability vector that maximizes the throughput of the queueing network. We show numerical results for some networks to quantify the quality of the routing vector approximations obtained.     

The superior fault tolerance of artificial neural network training with a fault/noise injectionbased genetic algorithm

Artificial neural networks (ANNs) are powerful computational tools that are designed to replicate the human brain and adopted to solve a variety of problems in many different fields. Fault tolerance (FT), an important property of ANNs, ensures their reliability when significant portions of a network are lost. In this paper, a fault/noise injection-based (FIB) genetic algorithm (GA) is proposed to construct fault-tolerant ANNs. The FT performance of an FIB-GA was compared with that of a common genetic algorithm, the back-propagation algorithm, and the modification of weights algorithm. The FIB-GA showed a slower fitting speed when solving the exclusive OR (XOR) problem and the overlapping classification problem, but it significantly reduced the errors in cases of single or multiple faults in ANN weights or nodes. Further analysis revealed that the fit weights showed no correlation with the fitting errors in the ANNs constructed with the FIB-GA, suggesting a relatively even distribution of the various fitting parameters. In contrast, the output weights in the training of ANNs implemented with the use the other three algorithms demonstrated a positive correlation with the errors. Our findings therefore indicate that a combination of the fault/noise injection-based method and a GA is capable of introducing FT to ANNs and imply that the distributed ANNs demonstrate superior FT performance.     

Pruning artificial neural networks using neural complexity measures

This paper describes a new method for pruning artificial neural networks, using a measure of the neural complexity of the neural network. This measure is used to determine the connections that should be pruned. The measure computes the information-theoretic complexity of a neural network, which is similar to, yet different from previous research on pruning. The method proposed here shows how overly large and complex networks can be reduced in size, whilst retaining learnt behaviour and fitness. The technique proposed here helps to discover a network topology that matches the complexity of the problem it is meant to solve. This novel pruning technique is tested in a robot control domain, simulating a racecar. It is shown, that the proposed pruning method is a significant improvement over the most commonly used pruning method Magnitude Based Pruning. Furthermore, some of the pruned networks prove to be faster learners than the benchmark network that they originate from. This means that this pruning method can also help to unleash hidden potential in a network, because the learning time decreases substantially for a pruned a network, due to the reduction of dimensionality of the network.     

Attack monitoring and localization in All-Optical Networks

An attacker’s connection can propagate quickly to different parts of a transparent All-Optical Network. Such attacks affect the normal traffic and cause a quality of service degradation or outright service denial. Attack monitors can collect the information of each link and each node to help diagnose the attacker’s exact location. Quick detection and localization of an attack source helps avoid losing large amounts of data in an all-optical network. However, to detect attack sources, it is not necessary to put monitors on all nodes. Since not every wavelength on every link is being used all the time, we propose to use the idle wavelengths to setup diagnostic connections and obtain the necessary information needed for diagnosis purposes. We show that placing a relatively small number of monitors at some key nodes in a network is sufficient to achieve level of performance. However, the monitor placement policy, routing policy, and diagnosis method are challenging problems. We, in this paper, first develop a monitor placement policy, a test connection policy, and a routing policy based on our definition of crosstalk attack and monitor node models. With these policies, we show that we can always detect and localize the malicious connections as long as there is no more than one malicious connection on each wavelength in the whole network. After this, we develop a scalable diagnosis method, which can localize the sources of the such malicious attacks in a fast manner. Arun K. Somani is currently Jerry R. Junkins Chair Professor of Electrical and Computer Engineering at Iowa State University. He earned his MSEE and Ph.D. degrees in electrical engineering from the McGill University, Montreal, Canada, in 1983 and 1985, respectively. He worked as Scientific Officer for Govt. of India, New Delhi from 1974 to 1982. From 1985 to 1997, he was a faculty member at the University of Washington, Seattle, WA, where he was a Professor of EE and CSE from 1995 onwards. From 1997 to 2002, he served as David C. Nicholas Professor of Electrical and Computer Engineering at Iowa State University. Professor Somani’s research interests are in the area of fault tolerant computing, computer communication and networks, wireless and optical networking, computer architecture, and parallel computer systems. Tao Wu received the B.S. and M.S.E.E. degrees in telecommunication engineering from the University of Electronic Science and Technology of China, Sichuan, China, in 1993 and 1996, respectively, and the Ph.D. degree in computer and electrical engineering from Iowa State University, Ames, in 2003. He is currently a Software Engineer with Microsoft Corporation. His research interests are in the area of WDM-based optical networking, network security, and image processing.     

Statistics of weighted brain networks reveal hierarchical organization and Gaussian degree distribution

Whole brain weighted connectivity networks were extracted from high resolution diffusion MRI data of 14 healthy volunteers. A statistically robust technique was proposed for the removal of questionable connections. Unlike most previous studies our methods are completely adapted for networks with arbitrary weights. Conventional statistics of these weighted networks were computed and found to be comparable to existing reports. After a robust fitting procedure using multiple parametric distributions it was found that the weighted node degree of our networks is best described by the normal distribution, in contrast to previous reports which have proposed heavy tailed distributions. We show that post-processing of the connectivity weights, such as thresholding, can influence the weighted degree asymptotics. The clustering coefficients were found to be distributed either as gamma or power-law distribution, depending on the formula used. We proposed a new hierarchical graph clustering approach, which revealed that the brain network is divided into a regular base-2 hierarchical tree. Connections within and across this hierarchy were found to be uncommonly ordered. The combined weight of our results supports a hierarchically ordered view of the brain, whose connections have heavy tails, but whose weighted node degrees are comparable.     

Identifying disease-associated biomarker network features through conditional graphical model

Biomarkers are often organized into networks, in which the strengths of network connections vary across subjects depending on subject-specific covariates (eg, genetic variants). Variation of network connections, as subject-specific feature variables, has been found to predict disease clinical outcome. In this work, we develop a two-stage method to estimate biomarker networks that account for heterogeneity among subjects and evaluate network's association with disease clinical outcome. In the first stage, we propose a conditional Gaussian graphical model with mean and precision matrix depending on covariates to obtain covariate-dependent networks with connection strengths varying across subjects while assuming homogeneous network structure. In the second stage, we evaluate clinical utility of network measures (connection strengths) estimated from the first stage. The second-stage analysis provides the relative predictive power of between-region network measures on clinical impairment in the context of regional biomarkers and existing disease risk factors. We assess the performance of proposed method by extensive simulation studies and application to a Huntington's disease (HD) study to investigate the effect of HD causal gene on the rate of change in motor symptom through affecting brain subcortical and cortical gray matter atrophy connections. We show that cortical network connections and subcortical volumes, but not subcortical connections are identified to be predictive of clinical motor function deterioration. We validate these findings in an independent HD study. Lastly, highly similar patterns seen in the gray matter connections and a previous white matter connectivity study suggest a shared biological mechanism for HD and support the hypothesis that white matter loss is a direct result of neuronal loss as opposed to the loss of myelin or dysmyelination.     

Microring Switching Control Using Plasmonic Ring Resonator Circuits for Super-Channel Use

The multi-wavelength selection and switching system using the hybrid plasmonic add-drop ring resonator (HPARR) for optical communication is proposed for multi-carrier super-channel-based designed. The plasmonic polariton technique applied in the ring resonator mode to the alternate waveguide interferometer switches the multi-wavelength laser emission in the various ranges. The combination of curvature-coupled plasmon ring and substances with different refractive index allows switching the multi-wavelength emission to shorter the free spectrum range (FSR) and specific wavelengths, without an applied pump signal or adjusted the ring size. It is suitable for the super-channel of wavelength division multiplex (WDM) in the future optical network.

     

A general mathematical performance model for wormhole-switched irregular networks

Irregular topologies are desirable network structures for building scalable cluster systems and very recently they have also been employed in SoC (system-on-chip) design. Many analytical models have been proposed in the literature to evaluate the performance of networks with different topologies such as hypercube, torus, mesh, hypermesh, Cartesian product networks, star graph, and k-ary n-cube; however, to the best of our knowledge, no mathematical model has been presented for irregular networks. Therefore, as an effort to fill this gap, this paper presents a comprehensive mathematical model for fully adaptive routing in wormhole-switched irregular networks. Moreover, since our approach holds no assumption for the network topology, the proposed analytical model covers all the aforementioned models (i.e. it covers both regular and irregular topologies). Furthermore, the model makes no preliminary assumption about the deadlock-free routing algorithm applied to the network. Finally, besides the generality of the model regarding the topology and routing algorithm, our analysis shows that the analytical model exhibits high accuracy which enables it to be used for almost all topologies with all traffic loads.     

A new family of Cayley graph interconnection networks based on wreath product and its topological properties

This paper introduces a new type of Cayley graphs for building large-scale interconnection networks, namely WG_n^2m\mathit{WG}_{n}^{2m}, whose vertex degree is m+3 when n≥3 and is m+2 when n=2. A routing algorithm for the proposed graph is also presented, and the upper bound of the diameter is deduced as ⌊5n/2⌋. Moreover, the embedding properties and maximal fault tolerance are also analyzed. Finally, we compare the proposed networks with some other similar network topologies. It is found that WG_n^2m\mathit{WG}_{n}^{2m} is superior to other interconnection networks because it helps to construct large-scale networks with lower cost.     

Modeling,analysis and simulation of ant-based network routing protocols

Using the metaphor of swarm intelligence, ant-based routing protocols deploy control packets that behave like ants to discover and optimize routes between pairs of nodes. These ant-based routing protocols provide an elegant, scalable solution to the routing problem for both wired and mobile ad hoc networks. The routing problem is highly nonlinear because the control packets alter the local routing tables as they are routed through the network. We mathematically map the local rules by which the routing tables are altered to the dynamics of the entire networks. Using dynamical systems theory, we map local protocol rules to full network performance, which helps us understand the impact of protocol parameters on network performance. In this paper, we systematically derive and analyze global models for simple ant-based routing protocols using both pheromone deposition and evaporation. In particular, we develop a stochastic model by modeling the probability density of ants over the network. The model is validated by comparing equilibrium pheromone levels produced by the global analysis to results obtained from simulation studies. We use both a Matlab simulation with ideal communications and a QualNet simulation with realistic communication models. Using these analytic and computational methods, we map out a complete phase diagram of network behavior over a small multipath network. We show the existence of both stable and unstable (inaccessible) routing solutions having varying properties of efficiency and redundancy depending upon the routing parameters. Finally, we apply these techniques to a larger 50-node network and show that the design principles acquired from studying the small model network extend to larger networks.     

Energy-aware routing considering load balancing for SDN: a minimum graph-based Ant Colony Optimization

Software-Defined Network (SDN) technology is a network management approach that facilitates a high level of programmability and centralized manageability. By leveraging the control and data plane separation, an energy-aware routing model could be easily implemented in the networks. In the present paper, we propose a two-phase SDN-based routing mechanism that aims at minimizing energy consumption while providing a certain level of QoS for the users’ flows and realizing the link load balancing. To reduce the network energy consumption, a minimum graph-based Ant Colony Optimization (ACO) approach is used in the first phase. It prunes and optimizes the network tree by turning unnecessary switches off and providing an energy-minimized sub-graph that is responsible for the network existing flows. In the second phase, an innovative weighted routing approach is developed that guarantees the QoS requirements of the incoming flows and routes them so that to balance the loads on the links. We validated our proposed approach by conducting extensive simulations on different traffic patterns and scenarios with different thresholds. The results indicate that the proposed routing method considerably minimizes the network energy consumption, especially for congested traffics with mice-type flows. It can provide effective link load balancing while satisfying the users’ QoS requirements.

     

Plasmonic Electro-Optical Switches Operating at Telecom Wavelengths

Electro-optical (EO) switches with a subwavelength device length based on metal–dielectric–metal nanocavities waveguide combined with organic EO materials have been proposed and numerically investigated. The finite difference time domain (FDTD) method with perfectly matched layer absorbing boundary condition is adopted to simulate and study their properties. The FDTD simulation results reveal that these structures filled with EO materials can realize the function of switch with low-driving voltage. The wavelength conversion switch structure might become a choice for the design of integrated architectures for optical computing and communication, especially in WDM systems in the nanoscale.     

Molecular basis for evolving modularity in the yeast protein interaction network

Scale-free networks are generically defined by a power-law distribution of node connectivities. Vastly different graph topologies fit this law, ranging from the assortative, with frequent similar-degree node connections, to a modular structure. Using a metric to determine the extent of modularity, we examined the yeast protein network and found it to be significantly self-dissimilar. By orthologous node categorization, we established the evolutionary trend in the network, from an “emerging” assortative network to a present-day modular topology. The evolving topology fits a generic connectivity distribution but with a progressive enrichment in intramodule hubs that avoid each other. Primeval tolerance to random node failure is shown to evolve toward resilience to hub failure, thus removing the fragility often ascribed to scale-free networks. This trend is algorithmically reproduced by adopting a connectivity accretion law that disfavors like-degree connections for large-degree nodes. The selective advantage of this trend relates to the need to prevent a failed hub from inducing failure in an adjacent hub. The molecular basis for the evolutionary trend is likely rooted in the high-entropy penalty entailed in the association of two intramodular hubs.     

A new measure of the robustness of biochemical networks

MOTIVATION: The robustness of a biochemical network is defined as the tolerance of variations in kinetic parameters with respect to the maintenance of steady state. Robustness also plays an important role in the fail-safe mechanism in the evolutionary process of biochemical networks. The purposes of this paper are to use the synergism and saturation system (S-system) representation to describe a biochemical network and to develop a robustness measure of a biochemical network subject to variations in kinetic parameters. Since most biochemical networks in nature operate close to the steady state, we consider only the robustness measurement of a biochemical network at the steady state. RESULTS: We show that the upper bound of the tolerated parameter variations is related to the system matrix of a biochemical network at the steady state. Using this upper bound, we can calculate the tolerance (robustness) of a biochemical network without testing many parametric perturbations. We find that a biochemical network with a large tolerance can also better attenuate the effects of variations in rate parameters and environments. Compensatory parameter variations and network redundancy are found to be important mechanisms for the robustness of biochemical networks. Finally, four biochemical networks, such as a cascaded biochemical network, the glycolytic-glycogenolytic pathway in a perfused rat liver, the tricarboxylic acid cycle in Dictyostelium discoideum and the cAMP oscillation network in bacterial chemotaxis, are used to illustrate the usefulness of the proposed robustness measure.