Dongming Chen

An Improved Image Encryption Algorithm Based on Chaos

An improved image encryption algorithm based on chaos is proposed for the degradation of periodicity, in which permutation is firstly carried out. Then the permuted image is encrypted by Chens chaotic system pixel by pixel and it makes the encryption more secure because the dynamical property of the Chens chaotic system is more complicated than the Lorenz chaotic system. The experiment results demonstrate that the presented algorithm improves the histogram distribution of the image.

A Feasible Chaotic Encryption Scheme for Image

Cryptography is an important technique to keep private data secretly in order to avoid unauthorized access. Traditional encryption methods such as DES, RSA etc... are widely used today and it seems to be computationally secure. Chaotic encryption is the new direction of cryptography and only few papers discuss on it, and it makes use of chaotic system properties such as sensitive to initial condition and lost of information. An image encryption scheme is proposed, in which shifting the positions and changing the grey values of image pixels are combined simultaneously to ensure a high level of security. Arnold cat map is used to permute the positions of the image pixels in the spatial domain. Then another chaotic logistic map is used to substitute the relationship between the ciphered image and the original image. An external 128 bit secret key is employed and is further modified after encrypting each pixel of the original image to make the encryption more robust against attacks. Sensitivity analysis, key space analysis and statistical analysis of several experimental results indicate the presented scheme has high security and superior quality, and thereby it is feasible.

A Modularity Degree Based Heuristic Community Detection Algorithm

A community in a complex network can be seen as a subgroup of nodes that are densely connected. Discovery of community structures is a basic problem of research and can be used in various areas, such as biology, computer science, and sociology. Existing community detection methods usually try to expand or collapse the nodes partitions in order to optimize a given quality function. These optimization function based methods share the same drawback of inefficiency. Here we propose a heuristic algorithm (MDBH algorithm) based on network structure which employs modularity degree as a measure function. Experiments on both synthetic benchmarks and real-world networks show that our algorithm gives competitive accuracy with previous modularity optimization methods, even though it has less computational complexity. Furthermore, due to the use of modularity degree, our algorithm naturally improves the resolution limit in community detection.

AES Key Expansion Algorithm Based on 2D Logistic Mapping

The shortage of original AES key generation and expansion strategy was researched. Based on these researches, the new AES key generation and expansion algorithms are proposed. In these algorithms, two-dimensional Logistic mapping is used to reduce the dependence between sub-keys. Experimental result shows that the security and robustness of the AES sub-keys have been strengthened.

Research of intrusion detection system based on vulnerability scanner

Feature matching is an important way of network intrusion detection system. With the increasing of attack types, rule database becomes more and more larger and the course of matching also becomes increasingly complex, which makes IDS easily to lose packets. For the problem above, two kinds of signature customization methods which based on open ports and CVE number are designed in this paper. These methods integrate the IDS and vulnerability scanner, help the misuse NIDS to select appropriate signature for the protected host and eliminate unnecessary signature matching through not modifying too much programs and setting the values of NIDS. Some experiments have been done on testing the integrated system. The results show that IDS can reduce the detection rules, useless alerts and improve detection efficiency after customization signature.

A community finding method for weighted dynamic online social network based on user behavior

Revealing the structural features of social networks is vitally important to both scientific research and practice, and the explosive growth of online social networks in recent years has brought us dramatic advances to understand social structures. Here we proposed a community detection approach based on user interaction behavior in weighted dynamic online social networks. We researched interaction behaviors in online social networks and built a directed and unweighted network model in terms of the Weibo following relationships between social individuals at the very beginning. In order to refine the interaction behavior, level one fuzzy comprehensive evaluation model was employed to describe how closely individuals are connected to each other. According to this intimate degree description, weights are tagged to the prior unweighted model we built. Secondly, a heuristic community detection algorithm for dynamic network was provided based on the improved version of modularity called module density. As for the heuristic rule, we chose greedy strategy and merely fed the algorithms with the changed parts within neighboring time slice. Experimental results show that the proposed algorithm can obtain high accuracy and simultaneously get comparatively lower time complexity than some typical algorithms. More importantly, our algorithm needs no a priori conditions.

Rethinking the Talents Cultivation Mode of Software Engineering

China’s economy continues to develop, the process for all walks of life gearing to international standards speeds up. As an important aspect of information technology, software engineering has already gotten high attention from all sides. Universities are the main training institution for cultivating advanced software talents, they are shouldered with the mission of training software talents who meet the requirements of the society. Software industry is a strong support for China’s national economy. How to strengthen and improve software engineering talents cultivation directly relates to development of information technology or even the economy of the whole society. This paper rethinks software engineering talents cultivation from specialty construction, innovative talents training, education of the age of big data, internationalization and excellent engineers plan.

An Algorithm for Identifying Useful Structure in Graphs Clustering

A new clustering method for graphs which is based on Breadth-First-Search is proposed. The clusters are of various structures such as cliques, stars, including hubs and outliers. Hubs and Outliers should be marked because they play very different roles in the network. Traditional algorithms may detect some useful structures, but they tend to fail to find hubs and outliers. We propose a clustering algorithm to solve the problem. Both synthetic networks and real datasets experiments demonstrate that apart from finding hubs and outliers, the algorithm also outperforms other algorithms in speed with a running time O(n) on a network with n nodes and m links, which is much faster than the fastest modularity-based algorithm O(mdlogn) (where d is the depth of the dendrogram describing the hierarchical cluster structure).

A High Capacity Spatial Domain Data Hiding Scheme for Medical Images

This paper presents a spatial steganography scheme with high steganography capacity for medical images. In the proposed scheme, four least significant bits of the cover image are used to hide secret information and a logistic mapping is employed to scramble cover image before embedding operation. In order to achieve minor degradation to cover medical image, the ROI region is excluded manually before embedding operation and an adaptive embedding strategy is employed. Experiment results show that, compared with Fan L’s hiding scheme, our proposed scheme can improve the steganography capacity by 2.25 times, and the maximum peak signal to noise ratio loss and average loss are only 0.78 and 0.45, respectively. In other words, our scheme does not only improve the capacity of the existing method but also can maintain an acceptable quality of the cover image.

Study on Chaotic Fault Tolerant Synchronization Control Based on Adaptive Observer

Aiming at the abrupt faults of the chaotic system, an adaptive observer is proposed to trace the states of the master system. The sufficient conditions for synchronization of such chaotic systems are also derived. Then the feasibility and effectiveness of the proposed method are illustrated via numerical simulations of chaotic Chen system. Finally, the proposed synchronization schemes are applied to secure communication system successfully. The experimental results demonstrate that the employed observer can manage real-time fault diagnosis and parameter identification as well as states tracing of the master system, and so the synchronization of master system and slave system is achieved.