Xiangjun Wu

Bio: Xiangjun Wu is an academic researcher from Henan University. The author has contributed to research in topics: Encryption & Synchronization (computer science). The author has an hindex of 24, co-authored 42 publications receiving 1882 citations. Previous affiliations of Xiangjun Wu include Potsdam Institute for Climate Impact Research & Humboldt University of Berlin.

A new color image encryption scheme based on DNA sequences and multiple improved 1D chaotic maps

Abstract: A DNA-based color image encryption method is proposed by using three 1D chaotic systems with excellent performance and easy implementation.The key streams used for encryption are related to both the secret keys and the plain-image.To improve the security and sensitivity, a division-shuffling process is introduced.Transforming the plain-image and the key streams into the DNA matrices randomly can further enhance the security of the cryptosystem.The presented scheme has a good robustness for some common image processing operations and geometric attack. This paper proposes a new encryption scheme for color images based on Deoxyribonucleic acid (DNA) sequence operations and multiple improved one-dimensional (1D) chaotic systems with excellent performance. Firstly, the key streams are generated from three improved 1D chaotic systems by using the secret keys and the plain-image. Transform randomly the key streams and the plain-image into the DNA matrices by the DNA encoding rules, respectively. Secondly, perform the DNA complementary and XOR operations on the DNA matrices to get the scrambled DNA matrices. Thirdly, decompose equally the scrambled DNA matrices into blocks and shuffle these blocks randomly. Finally, implement the DNA XOR and addition operations on the DNA matrices obtained from the previous step and the key streams, and then convert the encrypted DNA matrices into the cipher-image by the DNA decoding rules. Experimental results and security analysis show that the proposed encryption scheme has a good encryption effect and high security. Moreover, it has a strong robustness for the common image processing operations and geometric attack.

Chaos in the fractional order unified system and its synchronization

Abstract: The chaotic behaviors in the fractional order unified system are numerically investigated. By utilizing the fractional calculus techniques, we found that chaos exists in the fractional order unified system with order less than 3. The lowest order we found to have chaos in this system is 2.76. Chaos synchronization of the fractional order unified system is theoretically and numerically studied using the one-way coupling method. The suitable conditions for achieving synchronization of the fractional order differential system are derived by using the Laplace transform theory. It is noticed that the time required for achieving synchronization of the drive system and the response system and the synchronization effect sensitively depend on the coupling strength. Numerical simulations are performed to verify the theoretical analysis.

Modified generalized projective synchronization of a new fractional-order hyperchaotic system and its application to secure communication

Abstract: This paper presents a new fractional-order hyperchaotic system. The chaotic behaviors of this system in phase portraits are analyzed by the fractional calculus theory and computer simulations. Numerical results have revealed that hyperchaos does exist in the new fractional-order four-dimensional system with order less than 4 and the lowest order to have hyperchaos in this system is 3.664. The existence of two positive Lyapunov exponents further verifies our results. Furthermore, a novel modified generalized projective synchronization (MGPS) for the fractional-order chaotic systems is proposed based on the stability theory of the fractional-order system, where the states of the drive and response systems are asymptotically synchronized up to a desired scaling matrix. The unpredictability of the scaling factors in projective synchronization can additionally enhance the security of communication. Thus MGPS of the new fractional-order hyperchaotic system is applied to secure communication. Computer simulations are done to verify the proposed methods and the numerical results show that the obtained theoretic results are feasible and efficient.

Identifying influential nodes in large-scale directed networks: the role of clustering

Abstract: Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node’s neighbors but do not directly make use of the interactions among it’s neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors’ influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about nodes, more than 15 times faster than PageRank.