With the rapid development of Internet of Things (IoT), more and more IoT node devices are deployed, but the accompanying security problem cannot be ignored. Node devices at the network layer of IoT mainly communicate through wireless sensor networks. Compared with the Internet, they are more open and more vulnerable to network attacks such as denial of service. Aiming at the network layer security problem faced by wireless sensor networks, a network intrusion detection system based on sequence model was proposed to detect and alarm the network layer intrusion, which achieved higher recognition rate and lower false positive rate. Besides, aiming at the security problem of the node host device faced by wireless sensor network node devices, with the consideration of the node overhead, a host intrusion detection system based on simple sequence model was proposed. The experimental results show that, the two intrusion detection systems for the network layer and the host layer of wireless sensor network both have the accuracy more than 99%, and the false detection rate about 1%, which meet the industrial requirements. These two proposed systems can comprehensively and effectively protect the wireless sensor network security.

Many existing scheduling approaches in cloud data centers try to consolidate Virtual Machines (VMs) by VM live migration technique to minimize the number of Physical Machines (PMs) and hence minimize the energy consumption, however, it introduces high migration overhead; furthermore, the cost factor that leads to high payment cost for cloud users is usually not taken into account. Aiming at energy reduction for cloud providers and payment saving for cloud users, as well as guaranteeing the deadline of user tasks, a heuristic task scheduling algorithm called Energy and Deadline-Aware with Non-Migration Scheduling (EDA-NMS) was proposed. The execution of the tasks that have loose deadlines was postponed to avoid waking up new PMs and migration overhead, thus reducing the energy consumption. The results of extensive experiments show that compared with Proactive and Reactive Scheduling (PRS) algorithm, by selecting a smart VM combination scheme, EDA-NMS can reduce the static energy consumption and ensure the lowest payment with meeting the deadline requirement for key user tasks.

The existing Low-rank representation methods for face recognition fuse of local and global feature information of facial images inadequately. In order to solve the problem, a new face recognition method called Characterized Dictionary-based Low-Rank Representation (LRR-CD) was proposed. Firstly, every face image was represented as a set of characterized patches, then the low-rank reconstruction characteristic coefficients based on training samples as well as the corresponding intra-class characteristic variance were minimized. To obtain the efficient and high discriminative reconstruction coefficient matrix of face image patches, a new mathematical formula was presented. This formula could be used to completely preserve both global and local features of original hyper-dimensional face images, especially the local intra-class variance features, by the way of minimizing the low-rank constraint problem of corresponding patches in training samples and correlated intra-class variance dictionary. What's more, owing to the adequate mining of patch features, the proposed method obtained good robustness to the general noise such as facial occlusion and luminance variance. Several experiments were carried out on the face databases such as AR, CMU-PIE and Extended Yale B. The experimental results fully illustrate that the LRR-CD outperforms the compared algorithms of Sparse Representation Classification (SRC), Collaborative Representation Classification (CRC), LRR with Normalized CUT (LRR-NCUT) and LRR with Recursive Least Square (LRR-RLS), with the higher recognition rate of 2.58-17.24 percentage points. The proposed method can be effectively used for the global and local information fusion of facial features and obtains a good recognition rate.

The traditional information propagation model is more suitable for homogeneous network, and cannot be effectively applied to the non-homogeneous scale-free Social Network (SN). To solve this problem, an information propagation model based on local information was proposed. Topological characteristic difference between users and different effect on information propagation of user influence were considered in the model, and the probability of infection was calculated according to the neighbor nodes' infection and authority. Thus it could simulate the information propagation on real social network. By taking simulation experiments on Sina microblog networks, it shows that the proposed model can reflect the propagation scope and rapidity better than the traditional Susceptible-Infective-Recovered (SIR) model. By adjusting the parameters of the proposed model, it can verify the impact of control measures to the propagation results.

In this paper, an improved manifold distance based dissimilarity measure was designed to identify clusters in complex distribution and unknown reality data sets. This dissimilarity measure can mine the space distribution information of the data sets with no class labels by utilizing the global consistency between all data points. A K-medoids algorithm based on the improved manifold distance was proposed using the dissimilarity measure. The experimental results on eight artificial data sets with different structure and the USPS handwritten digit data sets indicate that the new algorithm outperforms or performs similarly to the other two K-medoids algorithms based on the existing manifold distance and Euclid distance and has the ability to identify clusters with simple or complex, convex or non-convex distribution.

To prevent the privacy disclosure caused by linking attack and reduce information loss resulting from anonymous protection, a (λα,k) multi-level anonymity model was proposed. According to the requirement of privacy preservation, sensitive attribute values could be divided into three levels: high, medium, and low. The risk of privacy disclosure was flexibly controlled by privacy protection degree parameter λ. On the basis of this, clustering-based approach for multi-level anonymization was proposed. The approach used a new hierarchical clustering algorithm and adopted more flexible strategies of data generalization for numerical attributes and classified attributes in a quasi-identifier. The experimental results show that the approach can meet the requirement of multi-level anonymous protection of sensitive attribute, and effectively reduce information loss.

In view of that the traditional decision tree algorithm that cannot solve the mass data mining and the multi-value bias problem of ID3 algorithm, the paper designed and realized a parallel decision tree classification algorithm based on the MapReduce framework. This algorithm adopted attribute similarity as the choice standard to avoid the multi-value bias problem of ID3 algorithm, and used the MapReduce model to solve the mass data mining problems. According to the experiments on the Hadoop cluster set up by ordinary PCs, the decision tree algorithm based on MapReduce can deal with massive data classification. What's more, the algorithm has good expansibility while ensuring the classification accuracy and can get close to linear speedup rate.

In order to deal with the frequently encountered non-stationary random signals in signal processing, they can be divided into sub-stationary random signals, and autocorrelation function can be used to reflect the essential characteristics of sub-stationary signals. The computation of piecewise stationary stochastic process autocorrelation function was discussed. In order to reduce the amount of calculation and errors of the existing function models, a new model to approximate autocorrelation function of piecewise stationary stochastic process was proposed in this paper. The computer simulation shows that the model can effectively approximate autocorrelation function. The computing speed is faster, and the errors are much fewer and smoother. Applying the model to the restoration of blurred digital images, a very good restoration effect can be got.