The key requirement for underwater image enhancement (UIE) is to overcome the unpredictable color degradation caused by the underwater environment and light attenuation, while addressing issues, such as color distortion, reduced contrast, and blurring. However, most existing unsupervised methods fail to effectively solve these problems, resulting in a visual disparity in metric-optimal qualitative results compared with undegraded images. In this work, we propose an implicit neural-guided cyclic generative model for UIE tasks, and the bidirectional mapping structure solves the aforementioned ill-posed problem from the perspective of bridging the gap between the metric-favorable and the perceptual-friendly versions. The multiband-aware implicit neural normalization effectively alleviates the degradation distribution. The U-shaped generator simulates human visual attention mechanisms, which enables the aggregation of global coarse-grained and local fine-grained features, and enhances the texture and edge features under the guidance of shallow semantics. The discriminator ensures perception-friendly visual results through a dual-branch structure via appearance and color. Extensive experiments and ablation analyses on the full-reference and nonreference underwater benchmarks demonstrate the superiority of our proposed method. It can restore degraded images in most underwater scenes with good generalization and robustness, and the code is available at https://github.com/SUIEDDM/INGC-GAN.

LEO constellation has received intensive research attention in the field of satellite communication. The existing centralized authentication protocols traditionally used for MEO/GEO satellite networks cannot accommodate LEO satellites with frequent user connection switching. This paper proposes a fast and efficient access verification protocol named BAVP by combining identity-based encryption and blockchain technology. Two different key management schemes with IBE and blockchain, respectively, are investigated, which further enhance the authentication reliability and efficiency in LEO constellation. Experiments on OPNET simulation platform evaluate and demonstrate the effectiveness, reliability, and fast-switching efficiency of the proposed protocol. For LEO networks, BAVP surpasses the well-known existing solutions with significant advantages in both performance and scalability which are supported by theoretical analysis and simulation results.

In response to the surging challenge in the number and types of mobile malware targeting smart devices and their sophistication in malicious behavior camouflage, we propose to compose a traffic behavior modeling method based on one-dimensional convolutional neural network with autoencoder and independent recurrent neural network (1DCAE-IndRNN) for mobile malware detection. The design solves the problem that most existing approaches for mobile malware traffic detection struggle with capturing the network traffic dynamics and the sequential characteristics of anomalies in the traffic. We reconstruct and apply the one-dimensional convolutional neural network to extract local features from multiple network flows. The autoencoder is applied to digest the principal traffic features from the neural network and is integrated into the independent recurrent neural network construction to highlight the sequential relationship between the highly significant features. In addition, the Softmax function with the LReLU activation function is adjusted and embedded to the neurons of the independent recurrent neural network to effectively alleviate the problem of unstable training. We conduct a series of experiments to evaluate the effectiveness of the proposed method and its performance for the 1DCAE-IndRNN-integrated detection procedure. The detection results of the public Android malware dataset CICAndMal2017 show that the proposed method achieves up to 98 detection accuracy and recall rates with clear advantages over other benchmark methods.

Internet backboned crowdsourcing utilizes network-wide resources to solve complicated and large-scale tasks, which are not accomplishable for independent individuals. Existing crowdsourcing platforms are mostly centralized solutions with reliability and trustworthiness fragile to single-point failures on the central servers. The innovation of distributed ledgers as blockchain inspires us to optimize the traditional crowdsourcing procedure with distributed sustainability. We propose a blockchain-based design of the distributed secure crowdsourcing scheme for task distribution and result verification without relying on any third trusted institution. A preference-based task distribution (PTD) mechanism is presented which guarantees the percentage of task distribution and the satisfaction of the chosen workers. Task works are continuously assessed for reputations based on their historical behaviors. Task completion correctness is verified by blockchain consensus in two different scenarios after workers submit their results with reputations. We implement a prototype system based on the Ethereum chain with PTD and solution verification components. With various tasks and scenarios evaluated in the system, the proposed distributed crowdsourcing framework shows system reliability, data security, and scenario feasibility.

With the advancement in big data and cloud computing technology, we have witnessed tremendous developments in applying intelligent techniques in network operation and management. However, learning- and data-based solutions for network operation and maintenance cannot effectively adapt to the dynamic security situation or satisfy administrators’ expectations alone. Anomaly detection of time-series monitoring indicators has been a major challenge for network administrative personnel. Monitored indicators in network operations are characterized by multiple instances with high dimensions and fluctuating time-series features and rely on system resource deployment and business environment variations. Hence, there is a growing consensus that conducting anomaly detection with machine intelligence under the operation and maintenance personnel’s guidance is more effective than solely using learning and modeling. This paper intends to model the anomaly detection task as a Markov Decision Process and adopts the Double Deep Q-Network algorithm to train an anomaly detection agent, in which the multidimensional temporal convolution network is applied as the principal structure of the Q network and the interactive guidance information from the operation and maintenance personnel is introduced into the procedure to facilitate model convergence. Experimental results on the SMD dataset indicate that the proposed modeling and detection method achieves higher precision and recall rates compared to other learning-based methods. Our method achieves model optimization by using human–computer interactions continuously, which guarantees a faster and more consistent model training procedure and convergence.