Due to the large number, wide distribution and complex environments of Internet of Things (IoT) devices, IoT is more vulnerable to DDoS (Distributed Denial of Service) attacks than traditional networks. Concerning this problem, a Distributed Denial of Service (DDoS) attack detection method based on Equal Length of Value Range K-means (ELVR- Kmeans) algorithm in Software Defined IoT (SD-IoT) architecture was proposed. Firstly, the centralized control characteristic of the SD-IoT controller was used to extract the flow tables of the OpenFlow switch to analyze the DDoS attack traffic characteristics in SD-IoT environment and extract the seven-tuple features related to the DDoS attack traffic. Secondly, the obtained flow tables were classified by the ELVR- Kmeans algorithm to detect whether a DDoS attack had occurred. Finally, the simulation experiment environment was built to test the detection rate, accuracy and error rate of the method. The simulation results show that the proposed method can effectively detect DDoS attacks in SD-IoT environment with detection rate and accuracy of 96.43% and 98.71% respectively, and error rate of 1.29%.

Aiming at the problem of low prediction accuracy in tectonic coal thickness prediction, a new method of Extreme Learning Machine (ELM) optimized by Particle Swarm Optimization (PSO) algorithm was proposed for predicting tectonic coal thickness. Firstly, Principal Component Analysis (PCA) was used to reduce the dimensionality of 3D seismic attributes, which reduced the dimension of seismic attributes, and eliminated the correlation among variables. Then, a Hybrid Kernel Extreme Learning Machine (HKELM) model with global polynomial kernel function and local Gaussian radial basis kernel function was constructed, and the kernel parameters of HKELM were optimized by using PSO algorithm. Furthermore, in order to solve the problem of easily falling into the local optimum for the PSO algorithm, the idea of simulated annealing, the inertia weight decreasing with the number of iterations, and the mutation operation based on reverse learning were added to the PSO algorithm, which made it easier jump out of local minimum points and get better results. In addition, in order to enhance the generalization ability of model, L2 regularization term was added based on the kernel function, which could effectively avoid the influence of noisy data and abnormal points on the generalization performance of model. Finally, the improved prediction model was applied to 15# coal seam in the central part of Luonan No.2 mining area in Xinjing Mining Area of Yangquan Coal Mine, and the predicted thickness of tectonic coal in the mining area guaranteed high consistency with the actual geological data. The experimental results show that the prediction error of the prediction model of tectonic coal thickness constructed by using the improved PSO algorithm to optimize HKELM is smaller, therefore the proposed method can be extended to the prediction of tectonic coal thickness in the actual mining area.

Due to a large amount of data and high dimension in currently network intrusion detection, a Moth-Flame Optimization (MFO) algorithm was applied to the feature selection of network intrusion detection. Since MFO algorithm converges fast and easy falls into local optimum, a Binary Moth-Flame Optimization integrated with Particle Swarm Optimization (BPMFO) algorithm was proposed. On one side, the spiral flight formula of the MFO algorithm was introduced to obtain strong local search ability. On the other side, the speed updating formula of the Particle Swarm Optimization (PSO) algorithm was combined to make the individual to move in the direction of global optimal solution and historical optimal solution, in order to increase the global convergence and avoid to fall into local optimum. By adopting KDD CUP 99 data set as the experimental basis, using three classifiers of Support Vector Machine (SVM), K-Nearest Neighbor ( KNN) and Naive Bayesian Classifier (NBC), Binary Moth-Flame Optimization (BMFO), Binary Particle Swarm Optimization (BPSO), Binary Genetic Algorithm (BGA), Binary Grey Wolf Optimization (BGWO) and Binary Cuckoo Search (BCS) were compared in the experiment. The experimental results show that, BPMFO algorithm has obvious advantages in the comprehensive performance including algorithm accuracy, operation efficiency, stability, convergence speed and jumping out of local optima when it is applied to the feature selection of network intrusion detection.

To tackle the higher requirement of mobile network for movie service system and the lack of description of movie domain knowledge, the necessity and feasibility of establishing the Movie Ontology (MO) were illustrated. Firstly, the objects and components of MO were summarized, and the principle and method for building the MO model were also put forward, with using the Web Ontology Language (OWL) and Protege 4.1 to build the model. After that, the concrete representation of the class, property, individual, axioms and inference rules in the MO were explained. Finally, the consistency of MO was analyzed, including the consistency analysis of relationship between classes and the consistency analysis based on axioms.