In the current detection of the malware family, the local features or global features extracted through the grayscale image of the malware cannot fully describe the malware. Aiming at the problem and to improve the detection effect, a malware detection method based on perceptual hash algorithm and feature fusion was proposed. Firstly, the grayscale image samples of malware were detected through the perceptual hash algorithm, and samples of specific malware families and uncertain malware families were quickly divided. Experimental tests showed that about 67% malwares were able to be detected by the perceptual hash algorithm. Then, the local features of Local Binary Pattern (LBP) and global features of Gist were further extracted for the samples of uncertain families, and the features of merging the above two features were used to classify and detect the malware samples by the machine learning algorithm. Finally, experimental results of the detection of 25 types of malware families show that the detection accuracy is higher when using the fusion feature of LBP and Gist compared to that when using a single feature only, and the proposed method is more efficient in classification and detection than the detection algorithm using machine learning only with the detection speed increased by 93.5%.

The image forgery method based on deep learning can generate images which are difficult to distinguish with the human eye. Once the technology is abused to produce fake images and videos, it will have a serious negative impact on a country's politics, economy, and culture, as well as the social life and personal privacy. To solve the problem, a Deepfake detection method based on autoencoder was proposed. Firstly, the Gaussian filtering was used to preprocess the image, and the high-frequency information was extracted as the input of the model. Secondly, the autoencoder was used to extract features from the image. In order to obtain better classification effect, an attention mechanism module was added to the encoder. Finally, it was proved by the ablation experiments that the proposed preprocessing method and the addition of attention mechanism module were helpful for the Deepfake image detection. Experimental results show that, compared with ResNet50, Xception and InceptionV3, the proposed method can effectively detect images forged by multiple generation methods when the dataset has a small sample size and contains multiple scenes, and its average accuracy is up to 97.10%, which is significantly better than those of the comparison methods, and its generalization performance is also significantly better than those of the comparison methods.

To solve the high storage cost and low retrieval speed problems in malicious Structure Query Language (SQL) detection faced by Nearest Neighbor (NN) method, a Hash learning based Malicious SQL Detection (HMSD) method was proposed. In this algorithm, Hash learning was used to learn the binary coding representation for SQL statements. Firstly, the SQL statements were presented as real-valued features by washing and deleting the duplicated SQL statements. Secondly, the isotropic hashing was used to learn the binary coding representation for SQL statements. Lastly, the retrieval procedure was performed and the detection speed was improved by using binary coding representation. Experimental results show that on the malicious SQL detection dataset Wafamole, the dataset is randomly divided so that the training set contains 10 000 SQL statements and the test set contains 30 000 SQL statements, at the length of 128 bits, compared with nearest neighbor method, the proposed algorithm has the detection accuracy increased by 1.3%, the False Positive Rate (FPR) reduced by 0.19%,the False Negative Rate (FNR) decreased by 2.41%, the retrieval time reduced by 94%, the storage cost dropped by 97.5%; compared with support vector machine method, the proposed algorithm has the detection accuracy increased by 0.17%, which demonstrate that the proposed algorithm can solve the problems of nearest neighbor method in malicious SQL detection.

Aiming at that Long Short Term Memory (LSTM) has only one pedestrian considered in isolation and cannot realize prediction with various possibilities, an attention mechanism based generative model for pedestrian trajectory prediction called AttenGAN was proposed to construct pedestrian interaction model and predict multiple reasonable possibilities. The proposed model was composed of a generator and a discriminator. The generator predicted multiple possible future trajectories according to pedestrian's past trajectory probability while the discriminator determined whether the trajectories were really existed or generated by the discriminator and gave feedback to the generator, making predicted trajectories obtained conform social norm more. The generator consisted of an encoder and a decoder. With other pedestrians information obtained by the attention mechanism as input, the encoder encoded the trajectories of the pedestrian as an implicit state. Combined with Gaussian noise, the implicit state of LSTM in the encoder was used to initialize the implicit state of LSTM in the decoder and the decoder decoded it into future trajectory prediction. The experiments on ETH and UCY datasets show that AttenGAN can provide multiple reasonable trajectory predictions and can predict the trajectory with higher accuracy compared with Linear, LSTM, S-LSTM (Social LSTM) and S-GAN (Social Generative Adversarial Network) models, especially in scenes of dense pedestrian interaction. Visualization of predicted trajectories obtained by the generator indicated the ability of this model to capture the interaction pattern of pedestrians and jointly predict multiple reasonable possibilities.

Concerning the huge calculation of sparse decomposition, a fast sparse decomposition algorithm with low computation complexity was proposed for first-order Polynomial Phase Signals (PPS). In this algorithm, firstly,two concatenate dictionaries including Df and Dp were constructed, and the atoms in the Df were constructed by the frequency, and the atoms in the Dp were constructed by the phase.Secondly, for the dictionary Df, the group testing was used to search the atoms that matched the signal, and the correlation values of the atoms and the signal were tested twice to achieve the reliability. Finally, according to the matching frequency atoms tested by group testing, the dictionary Dp was constructed, and the matching phase atoms were searched by Matching Pursuit (MP) algorithm. Therefore, the sparse decomposition of real first-order PPS was finished. The simulation results show that the computational efficiency of the proposed algorithm is about 604 times as high as that of matching pursuit and about 139 times as high as that of genetic algorithm, hence the presented algorithm has less computation complexity, and can finish sparse decomposition fast. The complexity of the algorithm is only O(N).

To deal with the problems of the strategies for selecting the global best position and the low local search ability, a multi-objective particle swarm optimization algorithm based on global best position adaptive selection and local search named MOPSO-GL was proposed. During the guiding particles selection in MOPSO-GL, the Sigma method and crowding distance of the particle in the archive were used and the archive member chose the guided particles in the swarm to improve the solution diversity and the swarm uniformity. Therefore, the population might get close to the true Pareto optimal solutions uniformly and quickly. Furthermore, the improved chaotic optimization strategy based on Skew Tent map was adopted, to improve the local search ability and the convergence of MOPSO-GL when the search ability of MOPSO-GL got weak. The simulation results show that MOPSO-GL has better convergence and distribution.

A block resource scheduling strategy for remote sensing images in multi-line server environment was proposed with the problems of huge amount of remote sensing data, heavy server load caused by multi-user concurrent requests which decreased the transmission efficiency of remote sensing images. To improve the transmission efficiency, an Improved Ant Colony Optimization (IACO) algorithm was used, which introduced a line waiting factor γ to dynamically select the optimal transmission lines. Intercomparison experiments among IACO, Ant Colony Optimization (ACO), Max-min, Min-min, and Random algorithm were conducted and IACO algorithm finished the tasks in the client and executed in the server with the shortest time, and the larger the amount of tasks, the more obvious the effect. Besides, the line resource utilization of IACO was the highest. The simulation results show that: combining multi-line server block scheduling strategy with IACO algorithm can raise the speed of remote sensing image transmission and the utilization of line resource to some degree.

In order to scientifically evaluate the performance of Space Information Data Processing System (SIDPS), this paper presented an evaluation method based on queuing network. The processing patterns of space information data were analyzed. In addition, core index systems of performance evaluation were constructed, and a performance evaluation model of SIDPS with limited waiting queuing network was established. The experimental results confirm the effectiveness of the approach.

Concerning the collision and access failure which are very likely to occur when the intensity of the users’ access increases, a random access packetbased strategy was proposed. The users were divided into several groups according to the strategy; certain subframes formed a superframe to enable different users to send Uplink Synchronization Code (SYNCUL) to the corresponding subframe of superframe when the users needed random access information, and based on it, priority was set for the users from different groups. Compared with the general strategy, packetbased strategy has greatly improved the success rate of the users’ access, and grouping based on priority was adopted to ensure a higher success rate for advanced users. The theoretical analysis and simulation results show that the proposed packetbased strategy can significantly improve the system Quality of Service (QoS) and could be an effective measurement for decreasing the probability of collision in random access.