arXiv subjects
Publications and source records attributed to Guanrong Chen.
In this paper, subgraphs and complementary graphs are used to analyze the network synchronizability. Some sharp and attainable bounds are provided for the eigenratio of the network structural matrix, which characterizes the network synchronizability, especially when the network's corresponding graph has cycles, chains, bipartite graphs or product graphs as its subgraphs.
Recently two encryption schemes were proposed by combining circular bit shift and XOR operations, under the control of a pseudorandom bit sequence (PRBS) generated from a chaotic system. This paper studies the security of these two encryption schemes and reports the following findings: 1) there exist some security defects in both schemes; 2) the underlying chaotic PRBS can be reconstructed as an equivalent key by using only two chosen plaintexts; 3) most elements in the underlying chaotic PRBS can be obtained by a differential known-plaintext attack using only two known plaintexts. Experimental results are given to demonstrate the feasibility of the proposed attack.
This paper addresses the synchronized region problem, which is reduced to a matrix stability problem, for complex dynamical networks. For any natural number $n$, the existence of a network which has $n$ disconnected synchronized regions is theoretically demonstrated. This shows the complexity in network synchronization. Convexity characteristic of stability for matrix pencils is further discussed. Smooth and generalized smooth Chua's circuit networks are finally discussed as examples for illustration.
In this paper, the investigation is first motivated by showing two examples of simple regular symmetrical graphs, which have the same structural parameters, such as average distance, degree distribution and node betweenness centrality, but have very different synchronizabilities. This demonstrates the complexity of the network synchronizability problem. For a given network with identical node dynamics, it is further shown that two key factors influencing the network synchronizability are the network inner linking matrix and the eigenvalues of the network topological matrix. Several examples are then provided to show that adding new edges to a network can either increase or decrease the network synchronizability. In searching for conditions under which the network synchronizability may be increased by adding edges, it is found that for networks with disconnected complementary graphs, adding edges never decreases their synchronizability. This implies that better understanding and careful manipulation of the complementary graphs are important and useful for enhancing the network synchronizability. Moreover, it is found that an unbounded synchronized region is always easier to analyze than a bounded synchronized region. Therefore, to effectively enhance the network synchronizability, a design method is finally presented for the inner linking matrix of rank 1 such that the resultant network has an unbounded synchronized region, for the case where the synchronous state is an equilibrium point of the network.
This paper addresses the energy accumulation problem, in terms of the $H_2$ norm, of linearly coupled dynamical networks. An interesting outer-coupling relationship is constructed, under which the $H_2$ norm of the newly constructed network with column-input and row-output shaped matrices increases exponentially fast with the node number $N$: it increases generally much faster than $2^N$ when $N$ is large while the $H_2$ norm of each node is 1. However, the $H_2$ norm of the network with a diffusive coupling is equal to $γ_2 N$, i.e., increasing linearly, when the network is stable, where $γ_2$ is the $H_2$ norm of a single node. And the $H_2$ norm of the network with antisymmetrical coupling also increases, but rather slowly, with the node number $N$. Other networks with block-diagonal-input and block-diagonal-output matrices behave similarly. It demonstrates that the changes of $H_2$ norms in different networks are very complicated, despite the fact that the networks are linear. Finally, the influence of the $H_2$ norm of the locally linearized network on the output of a network with Lur'e nodes is discussed.
Complex networks with expanding dimensions are studied, where the networks may be directed and weighted, and network nodes are varying in discrete time in the sense that some new nodes may be added and some old nodes may be removed from time to time. A model of such networks in computer data transmission is discussed. Each node on the network has fixed dimensionality, while the dimension of the whole network is defined by the total number of nodes. Based on the spectacular properties of data transmission on computer networks, some new concepts of stable and unstable networks differing from the classical Lyapunov stability are defined. In particular, a special unstable network model, called devil network, is introduced and discussed. It is further found that a variety of structures and connection weights affects the network stability substantially. Several criteria on stability, instability, and devil network are established for a rather general class of networks, where some conditions are actually necessary and sufficient. Mathematically, this paper makes a first attempt to rigorously formulate a fundamental issue of modeling discrete linear time-varying systems with expanding dimensions and study their basic stability property.
In this paper, some existing perceptual encryption algorithms of MPEG videos are reviewed and some problems, especially security defects of two recently proposed MPEG-video perceptual encryption schemes, are pointed out. Then, a simpler and more effective design is suggested, which selectively encrypts fixed-length codewords (FLC) in MPEG-video bitstreams under the control of three perceptibility factors. The proposed design is actually an encryption configuration that can work with any stream cipher or block cipher. Compared with the previously-proposed schemes, the new design provides more useful features, such as strict size-preservation, on-the-fly encryption and multiple perceptibility, which make it possible to support more applications with different requirements. In addition, four different measures are suggested to provide better security against known/chosen-plaintext attacks.
We study the problem of controlling a general complex network towards an assigned synchronous evolution, by means of a pinning control strategy. We define the pinning-controllability of the network in terms of the spectral properties of an extended network topology. The roles of the control and coupling gains as well as of the number of pinned nodes are also discussed.
We study geographical effects on the spread of diseases in lattice-embedded scale-free networks. The geographical structure is represented by the connecting probability of two nodes that is related to the Euclidean distance between them in the lattice. By studying the standard Susceptible-Infected model, we found that the geographical structure has great influences on the temporal behavior of epidemic outbreaks and the propagation in the underlying network: the more geographically constrained the network is, the more smoothly the epidemic spreads.
In this article, we propose a growing network model based on an optimal policy involving both topological and geographical measures. In this model, at each time step, a new node, having randomly assigned coordinates in a $1 \times 1$ square, is added and connected to a previously existing node $i$, which minimizes the quantity $r_i^2/k_i^α$, where $r_i$ is the geographical distance, $k_i$ the degree, and $α$ a free parameter. The degree distribution obeys a power-law form when $α=1$, and an exponential form when $α=0$. When $α$ is in the interval $(0,1)$, the network exhibits a stretched exponential distribution. We prove that the average topological distance increases in a logarithmic scale of the network size, indicating the existence of the small-world property. Furthermore, we obtain the geographical edge-length distribution, the total geographical length of all edges, and the average geographical distance of the whole network. Interestingly, we found that the total edge-length will sharply increase when $α$ exceeds the critical value $α_c=1$, and the average geographical distance has an upper bound independent of the network size. All the results are obtained analytically with some reasonable approximations, which are well verified by simulations.
We present a memory-based snowdrift game (MBSG) taking place on networks. We found that, when a lattice is taken to be the underlying structure, the transition of spatial patterns at some critical values of the payoff parameter is observable for both 4 and 8-neighbor lattices. The transition points as well as the styles of spatial patterns can be explained by local stability analysis. In sharp contrast to previously reported results, cooperation is promoted by the spatial structure in the MBSG. Interestingly, we found that the frequency of cooperation of the MBSG on a scale-free network peaks at a specific value of the payoff parameter. This phenomenon indicates that properly encouraging selfish behaviors can optimally enhance the cooperation. The memory effects of individuals are discussed in detail and some non-monotonous phenomena are observed on both lattices and scale-free networks. Our work may shed some new light on the study of evolutionary games over networks.
We study the interplay between evolutionary game and network structure and show how the dynamics of the game affect the growth pattern of the network and how the evolution of the network influence the cooperative behavior in the game. Simulation results show that the payoff-based preferential attachment mechanism leads to the emergence of a scale-free structural property, $P(k)\sim k^{-γ}$. Moreover, we investigate the average path length and the assortative mixing features. The obtained results indicate that the network has small-world and positive assortative behaviors, which are consistent with the observations of some real social networks. In parallel, we found that the evolution of the underlying network structure effectively promotes the cooperation level of the game. We also investigate the wealth distribution obtained by our model, which is consistent with the Pareto law in the real observation. In addition, the analysis of the generated scale-free network structure is provided for better understanding the evolutionary dynamics of our model.
As a powerful cryptanalysis tool, the method of return-map attacks can be used to extract secret messages masked by chaos in secure communication schemes. Recently, a simple defensive mechanism was presented to enhance the security of chaotic parameter modulation schemes against return-map attacks. Two techniques are combined in the proposed defensive mechanism: multistep parameter modulation and alternative driving of two different transmitter variables. This paper re-studies the security of this proposed defensive mechanism against return-map attacks, and points out that the security was much over-estimated in the original publication for both ciphertext-only attack and known/chosen-plaintext attacks. It is found that a deterministic relationship exists between the shape of the return map and the modulated parameter, and that such a relationship can be used to dramatically enhance return-map attacks thereby making them quite easy to break the defensive mechanism.
Recently, Pareek et al. proposed a symmetric key block cipher using multiple one-dimensional chaotic maps. This paper reports some new findings on the security problems of this kind of chaotic cipher: 1) a number of weak keys exists; 2) some important intermediate data of the cipher are not sufficiently random; 3) the whole secret key can be broken by a known-plaintext attack with only 120 consecutive known plain-bytes in one known plaintext. In addition, it is pointed out that an improved version of the chaotic cipher proposed by Wei et al. still suffers from all the same security defects.
Recently Lin et al. proposed a method of using the underdetermined BSS (blind source separation) problem to realize image and speech encryption. In this paper, we give a cryptanalysis of this BSS-based encryption and point out that it is not secure against known/chosen-plaintext attack and chosen-ciphertext attack. In addition, there exist some other security defects: low sensitivity to part of the key and the plaintext, a ciphertext-only differential attack, divide-and-conquer (DAC) attack on part of the key. We also discuss the role of BSS in Lin et al.'s efforts towards cryptographically secure ciphers.
In this article, we proposed a susceptible-infected model with identical infectivity, in which, at every time step, each node can only contact a constant number of neighbors. We implemented this model on scale-free networks, and found that the infected population grows in an exponential form with the time scale proportional to the spreading rate. Further more, by numerical simulation, we demonstrated that the targeted immunization of the present model is much less efficient than that of the standard susceptible-infected model. Finally, we investigated a fast spreading strategy when only local information is available. Different from the extensively studied path finding strategy, the strategy preferring small-degree nodes is more efficient than that preferring large-degree nodes. Our results indicate the existence of an essential relationship between network traffic and network epidemic on scale-free networks.
Many real networks are embedded in a metric space: the interactions among individuals depend on their spatial distances and usually take place among their nearest neighbors. In this paper, we introduce a modified susceptible-infected-susceptible (SIS) model to study geographical effects on the spread of diseases by assuming that the probability of a healthy individual infected by an infectious one is inversely proportional to the Euclidean distance between them. It is found that geography plays a more important role than hubs in disease spreading: the more geographically constrained the network is, the more highly the epidemic prevails.
In this Letter, we propose a growing network model that can generate scale-free networks with a tunable community strength. The community strength, $C$, is directly measured by the ratio of the number of external edges to internal ones; a smaller $C$ corresponds to a stronger community structure. According to the criterion obtained based on the master stability function, we show that the synchronizability of a community network is significantly weaker than that of the original Barabási-Albert network. Interestingly, we found an unreported linear relationship between the smallest nonzero eigenvalue and the community strength, which can be analytically obtained by using the combinatorial matrix theory. Furthermore, we investigated the Kuramoto model and found an abnormal region ($C\leq 0.002$), in which the network has even worse synchronizability than the uncoupled case (C=0). On the other hand, the community effect will vanish when $C$ exceeds 0.1. Between these two extreme regions, a strong community structure will hinder global synchronization.