Concerning the poor performance of the Single-Shot Refinement Neural Network for Object Detection (RefineDet) of the object detection network when detecting small sample classes in inter-class imbalanced datasets, a Subsection Weighted Loss (SWLoss) function was proposed. Firstly, the inverse of the number of samples from different classes in each training batch was used as the heuristic inter-class sample balance factor to weight the different classes in the classification loss, thus strengthening the concern on the small sample class learning. After that, a multi-task balancing factor was introduced to weight classification loss and regression loss to reduce the difference between the learning rates of two tasks. At last, experiments were conducted on Pascal VOC2007 dataset and dot-matrix character dataset with large differences in the number of target class samples. The results demonstrate that compared to the original RefineDet, the SWLoss-based RefineDet clearly improves the detection precision of small sample classes, and has the mean Average Precision (mAP) on the two datasets increased by 1.01 and 9.86 percentage points, respectively; and compared to the RefineDet based on loss balance function and weighted pairwise loss, the SWLoss-based RefineDet has the mAP on the two datasets increased by 0.68, 4.73 and 0.49, 1.48 percentage points, respectively.

At present, academic social network platforms have problems such as information overload and information asymmetry, which makes it difficult for scholars, especially those with low influence, to find contents they are interested in. At the same time, the scholars with high influence in the academic social network promote the formation of academic community and guide the scientific research of the scholars with low influence. Therefore, an Influential Scholar Recommendation Model based on Academic Community Detection (ISRMACD) was proposed to provide recommendation service for the scholars with low influence in academic social networks. First, the influential scholar group was used as the core structure of community to detect the academic community in complex network topological relationship generated by the relationship bonding — friendship among the scholars in the academic social network. Then the influences of scholars in the academic social network were calculated, and the recommendation service of influential scholars in the community was implemented. Experimental results on SCHOLAT dataset show that the proposed model achieves high recommendation quality under different influential scholar recommendation numbers, and has the best recommendation accuracy obtained by recommending 10 influential scholars each time, reaching 70% and above.

For the problem that the Rivest Cipher 4 (RC4) algorithm has invariant weak key, the randomness of the key stream sequence is not high and the initial state of the algorithm can be cracked, an improved RC4 algorithm based on elliptic curve was proposed. In the algorithm, the initial key was generated by using elliptic curve, Hash function and pseudo-random number generator, and a nonlinear transformation was performed under the action of the S-box and the pointer to finally generate a key stream sequence with high randomness. The randomness test carried out by National Institute of Standards and Technology (NIST) shows that the frequency test, run test and Maurer are 0.13893, 0.13081, and 0.232050 respectively higher than those of the original RC4 algorithm, which can effectively prevent the generation of invariant weak keys and resist the "sentence" attack. The initial key is a uniformly distributed random number without deviation, which can effectively resist the distinguishing attack. The elliptic curve and Hash function have one-way irreversibility, the pseudo-random number generator has high password strength, the initial key guess is difficult to assign and is not easy to crack, which can resist the state guessing attack. Theoretical and experimental results show that the improved RC4 algorithm is more random and safe than the original RC4 algorithm.

Those well-known types of intrusions can be detected with higher detection rate in the network at present, but it is very difficult to detect those new unknown types of network intrusions. In order to solve the problem, a network intrusion detection method of Deep Belief Network (DBN) model based on optimization of data processing was proposed. The data processing and method model were improved respectively without destroying the existing knowledge and increasing detection time seriously to solve the above problem. Firstly, the data processed by Probability Mass Function (PMF) encoding and MaxMin normalization was applied to the DBN model. Then, the relatively optimal DBN structure was selected through fixing other parameters, changing a parameter and the cross validation. Finally, the proposed method was tested on the benchmark NSL-KDD dataset. The experimental results show that, the optimization of data processing can improve the classification accuracy of the DBN model, the proposed intrusion detection method based on DBN has good adaptability and higher recognition ability of unknown samples. The detection time of DBN algorithm is similar to that of Support Vector Machine (SVM) algorithm and Back Propagation (BP) neural network model.

Aiming at the problem of slow convergence and weak real-time processing of large data in general Fuzzy C-Means (FCM) algorithm, an improved method of penalty factor on sample membership was proposed. Firstly, the characteristics of Suppressed Fuzzy C-Means (SFCM) clustering were analyzed, and the trigger condition for adjusting sample membership by penalty factor was studied, and then the dynamic membership adjusting scheme of SFCM based on penalty factor was designed. By using the algorithm, the samples are "moved to the poles" to achieve the purpose of rapid convergence. Theoretical analysis and experimental result show that under the same initial condition, the execution time efficiency of the improved algorithm is increased by 40% and 10% respectively compared with the traditional FCM and Optimal-Selection-based SFCM (OS-SFCM), at the same time, the clustering accuracy is also improved.

For balancing transparency and robustness of watermark, this paper proposed wavelet-domain digital watermarking method based on Fruit Fly Optimization Algorithm (FOA). The algorithm used Discrete Wavelet Transform (DWT) by FOA to watermarking technology and solved the contradiction between transparency and robustness in the watermark by swarm intelligence algorithm. In order to protect the copyright information of digital image, the selected original image was decomposed through a two-dimensional discrete wavelet transform, and watermark image through Arnold transformation was better embedded into wavelet coefficients of vertical sub-band, which guaranteed image quality. In the optimization process, the scaling factor was continuously being trained and updated by FOA. In addition, a new algorithm framework was proposed, which evaluated the scaling factor by prediction feasibility of DWT domain. The experimental results show that, the proposed algorithm has higher transparency and robustness against attacks, with watermarking similarity above 0.95, and 10% higher under geometric attacks such as rotation and shearing compared to some existing watermarking methods based on swarm intelligence.

Most of the P2P traffic identification methods have the problem of high time cost. Therefore, it was proposed to use TWin Support Vector Machine (TWSVM) whose time cost was a quarter of the common Support Vector Machine (SVM) to build classifier. Kmeans ensemble was used to create labeled sample set and labeled sample set was combined as the training sample of the TWSVM. At last, the constructed classification model was used to identify P2P traffic. The experimental results show that the method based on Kmeans and TWSVM can significantly decrease time cost of the P2P traffic identification, and has a higher accuracy rate and better stability than the standard SVM.

In order to improve the operation efficiency of big module RSA (Rivest-Shamir-Adleman) signature algorithm, four prime Chinese Remainder Theorem (CRT)-RSA digital signature was suggested in this paper. The Hash function SHA512 was used to produce message digest, and CRT combining with Montgomery algorithm was applied to optimize large number modular exponentiation. The security analysis and experiment show that the new algorithm can resist some common attacks, and it has some advantages in signature efficiency.

Concerning the overburdened third party problem of the present digital copyright protection protocol, this paper put forward a simple, efficient, secure copyright protection protocol and model to protect the buyer and seller's interests. Through two mechanisms of content server generating digital watermarking pool and mobile Agent dynamic distributing license, the protocol solved the security problems such as tolerance of conspiracy, middle attacks, hard disk cloning attacks and user treason. And it ensured the data security and integrity in the protocol entities exchange through encryption, authentication, digital signature and one-way permutation function. In addition, it still adopted the mechanism of buyer from arbitration, which enabled protocol more perfect and feasible.

The events correlation techniques in security integration management systems were introduced. A normal architecture of the correlation engine was introduced, and some discussions on the critical technologies and the main achievements in the field were put forward. The directions of the technology development were analyzed and evaluated, such as pattern obtainment, engine distribution and performance promotion. At last, a solution based on hierarchical rules to correlate events was presented.