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Adaptive Fine-grained Access Control Method in Social Internet of Things

International Journal of network security 2021年第1期23卷 42-48页

作者： Zhang, Hongbin Ma, Pengcheng Liu, Bin School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang China Hebei Key Laboratory of Network and Information Security Hebei Normal China School of Economics and Management Hebei University of Science China Technology Research Center of Big Data and Social Computing Hebei University of Science China

Social Internet of Things (SloT), as a new carrier of integration of social and Internet of Things, applies the research results of social networks from different aspects of the Internet of Things. Different types of connected intelligent objects interact socially, compared with random data access between them, access control technology is more stringent. This paper integrates social attributes into attributebased access control of Internet of Things, initializes relational attribute tags, and labels social interest attributes for different objects, then quantifes tag similarity and implements initial access control authorization, integrates social attributes into game theory to dynamically adjust access control policies, so the adaptive finegrained division of access control under the Social Internet of Things is effectively realized. The experimental results show that our method can not only effectively carry out initial authorization according to tag similarity, but also further adaptively adjust the permission policy according to social attributes, and further meet the finegrained partition requirements of access control, which is ensure the effective implementation of access control under the Social Internet of things. © 2021. All Rights Reserved.

关键词： Game theory

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Research on Monitoring Technology of 5G MEC Virtualization Environment

Research on Monitoring Technology of 5G MEC Virtualization E...

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Sustainable power and Energy Conference (iSPEC)

作者： Xiangqun Wang Jiaxuan Fei Wei Huang Wei Liu Global Energy Interconnection Research Institute Co. Ltd. State Grid Key Laboratory of Information & Network Security Nanjing China State Grid Jiangsu Electric Power CO. LTD. Research Institute Nanjing China

ISBN: (纸本)9781665414401

In view of the security risks in 5G network virtualization environment, this paper describes the security monitoring components of 5G MEC network virtual machine and container environment. By monitoring the host machine, Docker, container, KVM and other environments, the security status of operation permissions, network communication, file system, resource limitation, run operation, process operation and so on can be collected. The design scheme of the monitoring module and the realization method of the key function modules are introduced in detail, and the function verification is carried *** monitoring the virtualized environment, the security protection of MEC and other nodes can be effectively realized, the overall defense capability of 5G network in power system can be improved, and the existence of 5G network carrying power business can be seen, managed and controllable.

关键词： 5G mobile communication Software architecture Containers Virtual machining Safety power systems Internet

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A cooperative hybrid breeding swarm intelligence algorithm for feature selection

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Pattern Recognition 2026年 169卷

作者： Mengqing Mei Songsong Zhang Zhiwei Ye Mingwei Wang Wen Zhou Jia Yang Jixin Zhang Lingyu Yan Jun Shen School of Computer Science Hubei University of Technology Wuhan 430000 Hubei China Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network Wuhan 430000 Hubei China School of Computing and Information Technology University of Wollongong Wollongong 2500 New South Wales Australia

Feature selection (FS) is critical for reducing dimensionality and enhancing model accuracy but remains challenging in high-dimensional datasets. Metaheuristic algorithms, especially swarm intelligence, are often applied to optimization issues like FS, however, they struggle with the local optima and slow convergence in high-dimensional space. To address these issues, this paper proposes a cooperative hybrid breeding swarm intelligence algorithm (CHBSI), which simulates a hybrid breeding optimization algorithm based on heterosis theory, where the population is divided into three subpopulations for cooperative evolution. In each subpopulation, solutions are updated using a well-established particle swarm optimization algorithm, with Lévy flights integrated to enhance the global search capabilities and avoid the local optima. To further accelerate convergence during local optimization, a hyperbolic dynamic adjustment mechanism is introduced. Moreover, a classical ant colony optimization algorithm is incorporated to guide the global search via pheromone trails, thus improving exploration in complex search space. The best solutions from subpopulations are shared periodically, facilitating efficient information exchange. For binary FS, a regularized binary encoding is used to adapt the algorithm. The effectiveness of CHBSI is validated on 26 benchmark functions, and its binary version (bCHBSI) is tested on FS tasks across 18 UCI datasets. The experimental results show that both CHBSI and bCHBSI excel in convergence speed, fitness, classification accuracy, F1-score, the size of feature subset, and stability, significantly outperforming 10 advanced algorithms, including the S-shaped and V-shaped variants of CHBSI. CHBSI demonstrates scalability and robustness, maintaining superior performance across datasets with varying dimensionalities and complexities.

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Univariate Forecasting of time series data Based on Seq2Seq Model 20

Univariate Forecasting of time series data Based on Seq2Seq ...

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Proceedings of the 2020 4th International Conference on Electronic information Technology and Computer Engineering

作者： Yanwen Guo Tao Wu Yanbing Liu Yixin Jiang Department of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications Chongqing China Chongqing Engineering Laboratory of Internet and Information Sccurity Chongqing University of Posts and Telecommunications Chongqing China Electric Power Research Institute CSG Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)9781450387811

With the release of country's 2020 new infrastructure projects, the increasing demand for electricity in various projects has further promoted the intelligent upgrade of the power grid. The processing of grid data at the edge has become one of the future trends of next generation grid. In the field of power grid edge time series data prediction, the inability to obtain the variable characteristics of other systems and the uncertainty of the length of various edge input data, this paper proposes a Seq2seq prediction model based on a Bi-directional Long Short Term Memory (Bi-LSTM). Bi-LSTM can make full use of contextual information to achieve a more accurate prediction effect. The Seq2Seq model can process variable-length sequence data. In this paper, Seq2Seq model and BI-LSTM model are combined to solve the problem of uncertain edge length and univariate prediction. At the same time, in order to solve the information loss caused by Seq2Seq in the encoding stage, the Attention mechanism is introduced, it can compute the allocation of resources to more important tasks while addressing the problem of information overload. Through experimental research, it is found that the proposed ABSeq2Seq model is superior to other classic models in univariate prediction, and it has the better generalization for various types of data. We have proved its effectiveness in the single variable power prediction.

关键词： Time Series Bi-LSTM Seq2Seq Prediction Attention Mechanism Univariate

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Enhanced Competitive Swarm Optimizer for Multi-task Optimization 2020

Enhanced Competitive Swarm Optimizer for Multi-task Optimiza...

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Proceedings of the 2020 9th International Conference on computing and Pattern Recognition

作者： Wei Li Junqing Yuan Haonan Luo Zhou Lei Qingzheng Xu School of Computer Science and Engineering Xi'an University of Technology and Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China School of Computer Science and Engineering Xi'an University of Technology Xi'an China College of Information and Communication National University of Defense Technology Xi'an China

ISBN: (纸本)9781450387835

In the real world, many problems possess the high degree of similarity. Inspired by multifactorial inheritance model, evolutionary multi-task optimization paradigm is proposed to simultaneously solve multiple optimization problems. However, experimental results have revealed that the performance of multifactorial evolutionary algorithm deteriorates with negative knowledge transfer between uncorrelated tasks. To alleviate this issue, we proposed an enhanced competitive swarm optimizer to explore the generality of the multitasking paradigm. A new velocity update mechanism for losers is proposed to improve the search ability. Further, a mating approach is proposed to transfer implicit knowledge among tasks for generating offspring. Experimental and statistical analyses are performed on CEC2017 evolutionary multitask optimization competition. Results show that the proposed algorithm is competitive in comparison with other state-of-the-art multifactorial optimization algorithms.

关键词： Evolutionary multitasking competitive swarm optimizer evolutionary algorithms

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Performance Analysis for Cooperative Backscatter Communication Systems under Commensal Mode

Performance Analysis for Cooperative Backscatter Communicati...

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International Conference on Communication Technology (ICCT)

作者： Yingting Liu Yinghui Ye Jiaxiu Ma Tankun Wu Yongli Zhao State Grid Gansu Electric Power Research Institute Lanzhou China Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications Xi'an China School of Information Renmin University of China Beijing China College of Physics and Electronic Engineering Northwest Normal University Lanzhou China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665432078

In this letter, a cooperative backscatter communication system is considered, which consists of one primary transmitter, one primary receiver (PR), one backscatter device (BD) and one backscatter receiver. The considered system works in the commensal mode, in which the BD needs to adjust the reflection coefficient to satisfy the BR's requirement of successive interference cancellation. Based on the obtained distribution functions for the variable that is a product of the two variables with exponential distribution, the analytical expressions of the outage probability and ergodic capacity for the considered backscatter system are both derived. The numerical results verify the correction of our analytical results and exhibit that the derived results can evaluate the system performance more accurate compared to the existing results.

关键词： Communication systems Transmitters Exponential distribution System performance Receivers Probability power system reliability

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Fuzzified Federated Multi-task Unlearning for Efficient and Privacy-Preserving Swarm Consumer Electronics Systems

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IEEE Transactions on Consumer Electronics 2025年

作者： Qu, Y. Neil Kong, Xiangjie Shen, Shigen Zhang, Aiqing Gao, Huynh Thi Thanh Binh Longxiang Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250031 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250031 China Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Huzhou University School of Information Engineering Huzhou313000 China Anhui Normal University School of Physics and Electronic Information Wuhu China

The proliferation of consumer electronics systems in swarm environments has heightened the need for robust privacy-preserving techniques in federated learning (FL) frameworks. Traditional FL methods, while effective in distributing model training, often fall short in efficiently handling data removal requests and safeguarding against inference attacks. Existing solutions, such as FL with Differential Privacy (FL+DP), offer some improvements but still face challenges in balancing performance, privacy, and communication efficiency. In this paper, we introduce FL+Fuzzy Unlearning, a novel FL framework that integrates Fuzzy Logic to address these challenges in federated multi-task learning for swarm consumer electronics systems. Our method aims to maintain high model performance while ensuring robust privacy and efficient communication. We evaluated FL+Fuzzy Unlearning against Traditional FL and FL+DP using two benchmark datasets: MNIST and MovieLens. Our results demonstrate that the proposed approach attains comparable or superior accuracy (0.86 on MNIST and 0.88 on MovieLens) while achieving robust unlearning performance. Moreover, FL+Fuzzy Unlearning substantially enhances privacy protection, evidenced by elevated inference attack failure rates (0.75 for MNIST and 0.78 for MovieLens), and effectively reduces communication overhead, underscoring its efficiency in data exchange. Overall, FL+Fuzzy Unlearning presents a promising, balanced solution for privacy, efficiency, and adaptability, making it highly suitable for real-world applications in swarm consumer electronics systems. © 1975-2011 IEEE.

关键词： Differential privacy

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IEdroid:Detecting Malicious Android network Behavior Using Incremental Ensemble of Ensembles

IEdroid:Detecting Malicious Android Network Behavior Using I...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Cong Liu Anli Yan Zhenxiang Chen Haibo Zhang Qiben Yan Lizhi Peng Chuan Zhao Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan China School of Information Science and Engineering University of Jinan China School of Cyberspace Security (School of Cryptology) Hainan University China University of Otago Dunedin New Zealand Michigan State University USA

ISBN: (纸本)9781665408790

Malware detection has attracted widespread attention due to the growing malware sophistication. Machine learning based methods have been proposed to find traces of malware by analyzing network traffic. However, network traffic exhibits a series of growing and changing states, which makes it challenging to design a detection model that can detect malicious traffic over a long period without the need for costly retraining. In this paper, we present, IEdroid, an Android malicious network behavior detection method that leverages incremental ensembles for model update. Specifically, we train multiple classifiers to form an interim ensemble in distributed cluster environment, and update the interim ensemble by removing and adding classifiers. The generated model is composed of multiple interim ensembles that can adapt to the network traffic. We evaluated the performance of IEdroid using a dataset consisting of 98,565 benign and 41,267 malicious flows. Results show that IEdroid can effectively detect malicious traffic compared with state-of-the-art detection models. The experiment trained IEdroid on datasets incrementally for 10 times without a significant loss on accuracy, precision, recall, and F-Measure, compared with re-training from scratch with full data.

关键词： Training Adaptation models Distributed databases Prototypes Telecommunication traffic Machine learning Malware

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Research and Implementation of User Behavior Simulation Technology Based on power Industry Cyber Range

Research and Implementation of User Behavior Simulation Tech...

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information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Huiting Yang Tao Chen Yunxiao Bai Feng Li Mingxuan Li Ruxia Yang State Grid Xinjiang Electric Power Research Institute Urumqi China State Grid Xinjiang Electric Power Co. Ltd Urumqi China State Grid Key Laboratory of Information & Network Security Nanjing China Global Energy Interconnection Research Institute Co. Ltd Nanjing Jiangsu China

ISBN: (纸本)9781665428781

The current cyber range of the electric power industry has background traffic simulation problems, prospect behavior generation problems, and target simulation diversity problems in large-scale network environments. In response to these problems, this paper proposes a user behavior simulation technology suitable for power industry cyber range. We perform large-scale, high-fidelity network user behavior simulation by combining real traffic playback, inject user behavior traffic in the real network into the virtual target network, then model the user behavior in multiple scenarios in the power network space and generate it based on the method of sequence prediction, and finally realize the diversity simulation of user behavior.

关键词： Adaptation models Virtual links Diversity reception Telecommunication traffic Predictive models Traffic control Data models

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Research on Automated Penetration Testing Framework for power Web System Integrating Property information and Expert Experience

Research on Automated Penetration Testing Framework for Powe...

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作者： Zesheng Xi Jie Cui Bo Zhang Institute of Information and Communication Global Energy Interconnection Research Institute State Grid Key Laboratory of Information & Network Security State Grid Tianjin Electric Power Research Institute

Faced with the heterogeneous,intelligent and interconnected massive power web system environment,it is difficult to meet the needs of existing and incremental services in efficiency by mining and verifying vulnerabilities ***,an automated penetration testing framework for power Web system is *** property information and expert experience,the framework guides vulnerability mining path and vulnerability exploiting method selection,and realizing automatic vulnerability verification and exploitation of Web system through autonomous decision *** experimental results show that the framework can simulate real attack behavior and efficiently verify and exploit common Web system vulnerabilities.

关键词： Vulnerability exploitation autonomous decision property information expert experience

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