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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper investigated the issue of covert network attacks on video surveillance systems in light of their networking and intelligent development, which brought about new risks. The primary objective of this research was to explore numerous cases of covert network attacks and summarize the specific characteristics of such attacks targeting video surveillance systems. Additionally, this paper examined the unique advantages of honeypot technology in detecting network attack behaviors and identifying attack clues, and outlined the honeypot defense methods against covert attacks on video surveillance systems. Furthermore, considering the shortcomings in the visual scene deployment of video surveillance honeypots, this paper introduced a deep scene fabrication defense framework that combined generative Al models with video surveillance honeypots. Finally, it proposed the development direction of honeypot defense technology for video surveillance systems. [ABSTRACT FROM AUTHOR]

Copyright of Science Technology & Engineering is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Mechanical & Electronic Control Engineering is the property of Omniscient Pte. Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science Technology & Engineering is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Predictive analysis of cloud resources is very important for responding to resource requests and making timely decisions. For resource management issues such as over-allocation and insufficient supply of container cloud resources, low cloud resource prediction accuracy and data volatility issues, in order to make cloud resource predictions work load responding to the demand in advance and making a reasonable allocation, a cloud resource combination forecasting model was proposed that based on Holt-Winters and long short-term memory network (HW-LSTM), and the coefficient of variation of the forecast residuals was used to give weights. Prediction experiments on Amazon's CPU data set show that the combined model has better prediction performance and stability than Holt-Winters, LSTM and convolutional neural networks(CNN) models. The optimization ranges of root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), mean absolute scaled error ( MASE) and variance D( n) are 0. 065 ~ 1. 026, 0. 023 ~ 0. 269, 0. 001 ~ 0. 007, 0. 004 ~ 0. 039 and 0. 079 ~ 4. 125 respectively. [ABSTRACT FROM AUTHOR]

Copyright of Science Technology & Engineering is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science Technology & Engineering is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To improve the clustering of the clustering algorithm, the bionic optimization algorithm and the kmeans clustering algorithm are combined to realize data clustering (BFOA-K). In the clustering process, the fruit fly optimization algorithm is used to determine the centroid of the k-means clustering algorithm, and the k-means clustering algorithm is used for data clustering. To solve the problem that k-means is sensitive to the initial centroid and easy to fall into the local optimum, and the influence of the fruit fly optimization algorithm on the flight step length, the F distribution is used to dynamically change the step length which improves the global search ability of the algorithm. The elite retention strategy is adopted to increase the diversity of the fruit fly population, expand the scope of the search and improve the search efficiency. Four UCI standard datasets are used to test the algorithm. The experiment shows that the algorithm is better than other comparison algorithms in all clustering evaluation indicators. The BFOA-K improves the convergence of the algorithm with effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

For workflow scheduling in hybrid cloud environment, a new workflow scheduling algorithm combining slime mold algorithm and particle swarm optimization algorithm was proposed. The advantages of particle swarm optimization can guide the poor solution and accelerate the convergence of the poor solution. The solution with good quality is locally mutated to avoid falling into local optimum. At the same time, the crossover operator is added to refine the search strength of the algorithm in the task scheduling problem. According to the workflow scheduling model in hybrid cloud environment, discrete coding is carried out to complete the optimization of multiple scheduling objectives. The simulation results show that the proposed algorithm has better optimization performance than the single heuristic algorithm and the other improved algorithm. [ABSTRACT FROM AUTHOR]

In order to improve the security in cloud computing environment and the reliability of transactions in cloud computing environment,a three-layer cloud computing trust model based on multi-Agent system to solve the transaction trust problem between users and cloud service providers is proposed in this paper. According to the characteristics of cloud computing environment and multi-Agent system, the model is composed of three layers of trust management Agent, service management Agent and trust evaluation Agent, with three layers of trust management model. It combines the three trust modes and the trust model selection method according to historical transaction records to judge the trust degree in cloud computing transactions. Simulation experiments prove that the model presented in this paper is superior to the other model in testing multi-Agent transactions and multi-Agent node security. The model presented in this paper can reduce fraudulent behaviors in user transactions in cloud computing environment to a certain extent, reduce user losses, and improve the security degree of transaction trust in cloud environment. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)