Using deep learning and machine learning techniques for network intrusion detection is of great significance for enhancing the defense capability of network security systems. Given the characteristics of generative adversarial networks, such as the approximate consistency of generated samples with the input data distribution but with a random distribution within a certain bounded interval, and in response to the problem of insufficient classification performance and detection omission caused by the imbalance of different degrees of data categories and quantities in network intrusion traffic, and in light of the fact that the effectiveness of existing classification algorithms based on unbalanced traffic data still has some room for improvement, this paper proposes a network intrusion detection strategy based on auxiliary classifier generative adversarial networks. The data expansion experiments are conducted with the intrusion detection dataset NSL-KDD. The data are classified into twenty-three categories before and after the expansion by binary classification validation. The results show that the expansion of the generated samples for unbalanced network traffic data improve the subsequent recognition effect significantly. Finally, five classification performance index verification experiments are conducted. The results prove that the strategy of this paper performs better in accuracy, precision, recall rate and F-value indexes, and is capable of obtaining a large number of features from limited samples and inferring complete data distribution based on fewer features. The model as a whole has stronger generalization ability and defense effect.

The purpose of this article is to utilize adaptive dynamic programming to solve an optimal consensus problem for double‐integrator multiagent systems with completely unknown dynamics. In double‐integrator multiagent systems, flocking algorithms that neglect agents' inertial effect can cause unstable group behavior. Despite the fact that an inertias‐independent protocol exists, the design of its control law is decided by dynamics and inertia. However, inertia in reality is difficult to measure accurately, therefore, the control gain in the consensus protocol was solved by developing adaptive dynamic programming to enable the double‐integrator systems to ensure the consensus of the agents in the presence of entirely unknown dynamics. Firstly, we demonstrate in a typical example how flocking algorithms that ignore the inertial effect of agents can lead to unstable group behavior. And even though the protocol is independent of inertia, the control gain depends quite strongly on the inertia and dynamic of the agent. Then, to address these shortcomings, an online policy iteration‐based adaptive dynamic programming is designed to tackle the challenge of double‐integrator multiagent systems without dynamics. Finally, simulation results are shown to prove how effective the proposed approach is.

Visual tracking remains an open challenge, as it requires real-time and long-term accurate target prediction. Siamese network has been widely studied due to its excellent accuracy and speed. Since long-term tracking may lead to model degradation and drift, most existing algorithms cannot well solve this problem. This article proposes a new Siamese Network based on Fast Attention Network named SiamFA. This method designs an attention model, which can enhance the key and global information of the target, to obtain a more robust target model and achieve long-term tracking. At the same time, the attention model is used to obtain the potential position information of the target when calculating the similarity between the template and the search area. In addition, the attention network we design reduces many redundant operations and effectively improves computational efficiency. We utilize a multi-layer perceptron to forecast the bounding box to avoid excessive hyper-parameters. In order to verify the effectiveness of our network, we conduct tests on many commonly used datasets, such as OTB100, GOT-10k, LaSOT, TrackingNet, UAV123. Our method can achieve a success rate of 62.7% and the precision rate of 64.3% on LaSOT. At the same time, it can run at about 100fps, which exceeds the comparison network, proving that our network can run in real-time.

The purpose of this paper is to utilize adaptive dynamic programming to solve an optimal consensus problem for double-integrator multi-agent systems with completely unknown dynamics. In double-integrator multi-agent systems, flocking algorithms that neglect agents’ inertial effect can cause unstable group behavior. Despite the fact that an inertias-independent protocol exists, the design of its control law is decided by dynamics and inertia. However, inertia in reality is difficult to measure accurately, therefore, the control gain in the consensus protocol was solved by developing adaptive dynamic programming to enable the double-integrator systems to ensure the consensus of the agents in the presence of entirely unknown dynamics. Firstly, we demonstrate in a typical example how flocking algorithms that ignore the inertial effect of agents can lead to unstable group behavior. And even though the protocol is independent of inertia, the control gain depends quite strongly on the inertia and dynamic of the agent. Then, to address these shortcomings, an online policy iteration-based adaptive dynamic programming is designed to tackle the challenge of double-integrator multi-agent systems without dynamics. Finally, simulation results are shown to prove how effective the proposed approach is.

The challenging issues in infrared and visible image fusion (IVIF) are extracting and fusing as much useful information as possible contained in the source images, namely, the rich textures in visible images and the significant contrast in infrared images. Existing fusion methods cannot address this problem well due to the handcrafted fusion operations and the extraction of features only from a single scale. In this work, we solve the problems of insufficient information extraction and fusion from another perspective to overcome the difficulties in lacking textures and unhighlighted targets in fused images. We propose a multi-scale feature extraction (MFE) and joint attention fusion (JAF) based end-to-end method using a generative adversarial network (MJ-GAN) framework for the aim of IVIF. The MFE modules are embedded in the two-stream structure-based generator in a densely connected manner to comprehensively extract multi-grained deep features from the source image pairs and reuse them during reconstruction. Moreover, an improved self-attention structure is introduced into the MFEs to enhance the pertinence among multi-grained features. The merging procedure for salient and important features is conducted via the JAF network in a feature recalibration manner, which also produces the fused image in a reasonable manner. Eventually, we can reconstruct a primary fused image with the major infrared radiometric information and a small amount of visible texture information via a single decoder network. The dual discriminator with strong discriminative power can add more texture and contrast information to the final fused image. Extensive experiments on four publicly available datasets show that the proposed method ultimately achieves phenomenal performance in both visual quality and quantitative assessment compared with nine leading algorithms.