The key network node identification technology plays an important role in comprehending unknown terrains and rapid action planning in network attack and defense confrontation. The conventional key... Show More

The key network node identification technology plays an important role in comprehending unknown terrains and rapid action planning in network attack and defense confrontation. The conventional key node identification algorithm only takes one type of relationship into consideration; therefore, it is incapable of representing the characteristics of multiple relationships between nodes. Additionally, it typically disregards the periodic change law of network node vulnerability over time. In order to solve the above problems, this paper proposes a network key node identification method based on the vulnerability life cycle and the significance of the network topology. Based on the CVSS score, this paper proposes the calculation method of the vulnerability life cycle risk value, and identifies the key nodes of the network based on the importance of the network topology. Finally, it demonstrates the effectiveness of the method in the selection of key nodes through network instance analysis.

The development of deep convolutional neural networks has been largely responsible for the significant strides forward made in steganography over the past decade. In the field of image... Show More

The development of deep convolutional neural networks has been largely responsible for the significant strides forward made in steganography over the past decade. In the field of image steganography, generative adversarial networks (GAN) are becoming increasingly popular. This study describes current development in image steganographic systems based on deep learning. The authors' goal is to lay out the various works that have been done in image steganography using deep learning techniques and provide some notes on the various methods. This study proposed a result that could open up some new avenues for future research in deep learning based on image steganographic methods. These new avenues could be explored in the future. Moreover, the pros and cons of current methods are laid out with several promising directions to define problems that researchers can work on in future research avenues.

In order to improve the efficiency and quality of software development, automatic code generation technology is the current focus. The quality of the code generated by the automatic code generation... Show More

In order to improve the efficiency and quality of software development, automatic code generation technology is the current focus. The quality of the code generated by the automatic code generation technology is also an important issue. However, existing metrics for code automatic generation ignore that the programming process is a continuous dynamic changeable process. So the metric is a dynamic process. This article proposes a metric method based on dynamic abstract syntax tree (DAST). More specifically, the method first builds a DAST through the interaction in behavior information between the automatic code generation tool and programmer. Then the measurement contents are extracted on the DAST. Finally, the metric is completed with contents extracted. The experiment results show that the method can effectively realize the metrics of automatic code generation. Compared with the MAST method, the method in this article can improve the convergence speed by 80% when training the model, and can shorten the time-consuming by an average of 46% when doing the metric prediction.

Aiming at the problem that abnormal behavior is difficult to distinguish from normal behavior, a retrieval method for abnormal behavior of laboratory security surveillance video based on deep... Show More

Aiming at the problem that abnormal behavior is difficult to distinguish from normal behavior, a retrieval method for abnormal behavior of laboratory security surveillance video based on deep automatic encoder is proposed. Firstly, the fuzzy median filtering algorithm is used to reduce the noise of the collected laboratory security surveillance video, and then the YUV spatial chromaticity difference method is used to divide the foreground and background of the video, and the illumination degree in the video is determined. The diagonal model and codebook clustering idea are used to compensate for global and local lighting mutations. Finally, the preprocessed video is input into the mixture model, which is based on the deep automatic encoder and combined with the Gaussian mixture model, and the abnormal behavior retrieval results are output. The experimental results show that the proposed method has good security surveillance video preprocessing effect, large AUC, small error rate of abnormal behavior retrieval, and high operation efficiency.

The abundance of digital data within modern vehicles makes digital vehicle forensics (DVF) a promising subfield of digital forensics (DF), with significant potential for investigations. In this... Show More

The abundance of digital data within modern vehicles makes digital vehicle forensics (DVF) a promising subfield of digital forensics (DF), with significant potential for investigations. In this research, the authors apply DVF methodology to a SUV, simulating a real case by extracting and analyzing the data in the period leading up to an incident to evaluate the effectiveness of DVF in solving crime. The authors employ DVF approach to extract data to reveal evidential information for judicial evaluation and verdict. This data helped determine whether the incident represented an accident or an act of crime. This simulated case and the assumptions supported by the DVF evidence provides a compelling example of how law enforcement agencies can leverage DVF to collect and present evidence to relevant authorities. This form of forensics can assist government in planning for and regulating the deployment of DVF data, the judiciary in assessing the nature and admissibility of evidence, and vehicle manufacturers in complying with the regulations relating to the harvesting and retrieval of data.

In medical data sharing, the data access control authorities of the sharing entities and computing capabilities of the sharing platforms are asymmetric. This asymmetry leads to poor patient control... Show More

In medical data sharing, the data access control authorities of the sharing entities and computing capabilities of the sharing platforms are asymmetric. This asymmetry leads to poor patient control over their data, privacy disclosure, and difficulties in tracking data sharing. This aarticle proposes a cooperation model of cloud and chain (CMCC) for the secure sharing of medical data. In the CMCC, the power equivalence of blockchain nodes limits the control authority asymmetry between doctors and patients in medical data sharing. Moreover, a cloud server is used to store medical data, and some of the node-side computations are handed over to the cloud, which addresses the asymmetric computing capability asymmetry between the cloud and ordinary nodes. Based on the CMCC, a secure medical data sharing scheme based on proxy re-encryption mechanism is proposed. This scheme realizes secure medical data sharing, especially the patient's complete control of the data. The security and performance analysis show that the proposed scheme outperforms the existing ones.

Cloud computing involves transferring data to remote data centers for processing, which consumes significant network bandwidth and transmission time. Edge computing can effectively address this... Show More

Cloud computing involves transferring data to remote data centers for processing, which consumes significant network bandwidth and transmission time. Edge computing can effectively address this issue by processing tasks at edge nodes, thereby reducing the amount of data transmitted and enhancing the utilization of network bandwidth. This paper investigates intelligent task offloading under the three-layer architecture of cloud-edge-device to fully exploit the cloud-edge collaboration potential. Specifically, an optimization objective function is constructed by modelling the processing cost of all computing tasks. Additionally, asynchronous advantage actor-critic (A3C) algorithm is proposed under cloud-edge collaboration to solve the optimization problem of minimizing the sum of the weights of task offloading delay and energy consumption. Experimental results indicate that the algorithm can effectively utilize the computing resources of the cloud center, reduce task execution delay and energy consumption, and compare favourably with three existing task offloading methods.

When responding to emergencies such as sudden natural disasters, communication networks face challenges such as network traffic surge and complex geographic environments. Aiming at the problems of... Show More

When responding to emergencies such as sudden natural disasters, communication networks face challenges such as network traffic surge and complex geographic environments. Aiming at the problems of high transmission delay and insensitivity to user's preference in the current UAV edge caching strategy, this paper proposes a UAV caching content recommendation algorithm based on graph neural network. Firstly, the location of UAV is determined by clustering algorithm; secondly, the interest preferences of user nodes in the cluster are predicted by GCLRSAN model, and the UAV cache content is designed according to the result; finally, simulation experiments show that the model and algorithm proposed in this paper can effectively reduce the backhaul link overhead and outperform the comparison algorithms in the indexes such as accuracy rate, recall rate, cache hit rate, and transmission delay.

The internet brings high efficiency and convenience to society; however, the issue of information security in network communication has significantly affected every aspect of the society. How to... Show More

The internet brings high efficiency and convenience to society; however, the issue of information security in network communication has significantly affected every aspect of the society. How to ensure the security of this network communication information has become an important research topic. This paper proposes a diagnosis and prediction method based on cyber-physical fusion and deep learning, such as LSTM and CNN, to diagnose and predict network security in a complex network environment. The experiment results showed that the accuracy of network security diagnosis of the LSTM method in the training set was approximately 80%/ After the CNN training process, it has the highest accuracy rate of 95% on the test data set. This paper analysed the nature of network security problems from the perspective of cyber-physical fusion. CNN-based method to diagnose network security can obtain results with a higher accuracy rate so that technicians can better take measures to protect network security.