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IEEE International Conference on Communications (ICC)

作者： Zhutao Liu Chao Qiu Yunfeng Zhao Xiaofei Wang Jing Jiang College of Intelligence and Computing Tianjin University Tianjin China Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications Xi'an China

computing power network (CPN) is an evolution of multi-access edge computing. Since the skyrocketing proliferation of CPN s, energy consumption aggravates explosively. However, majority of energy is wasted due to the incomplete analysis of tasks and resources, such as coarse-grained tasks consideration, coarse-grained resources integration, and unfocused complex information. In this paper, we propose a broad attention based fine-grained task offloading approach in green CPNs, i.e., Bat-FG. Specifically, for fine-grained tasks, we establish directed acyclic graphs (DAGs) subtasks offloading problem for green CPNs under the dependency and service constraints. For finegrained resources, decentralized resources are integrated into resource pools. Bridging the gap between fine-grained tasks and resource pools, we design a novel broad attention meta-reinforcement learning approach, i.e., Bat-MRL to focus on the main information for reducing the tasks' latency and energy consumption. Finally, extensive simulations show that Bat-FG significantly reduces 25.6 % task latency and 72.9 % energy consumption.

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Mobile Terminal security Protection Method Based on Community Detection Algorithms and Graph Matching networks 8th

Mobile Terminal Security Protection Method Based on Communit...

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8th International Conference on Artificial Intelligence and security , ICAIS 2022

作者： Chen, Mu Dai, Zaojian Li, Yong Zhang, Juling Wang, Sheng Zhou, Jian Wu, Fan Institute of Information and Communication Global Energy Interconnection Research Institute Nanjing210003 China State Grid Key Laboratory of Information and Network Security Nanjing210003 China Electric Power Research Institute of State Grid Sichuan Chengdu610041 China State Grid Sichuan Electric Power Company Chengdu610041 China Computer Science Department Tuskegee University TuskegeeAL36088 United States

ISBN: (纸本)9783031067631

The rapid growth in the number of malware apps poses a great danger to users of power mobile terminals. Among smart devices, devices using the Android system are very popular and thus are easy targets for malware attacks. In this paper, we propose a mobile terminal security protection method based on community detection algorithms and graph matching networks. First, the call relationships between functions are extracted from the decompiled APK files, and runtime information is collected as a supplement to complete the construction of the behavior graph. Second, based on the behavior graph of malware samples, we propose a method to extract the public behavior graph as the features of malware families. Finally, a graph matching network is used to learn and calculate the similarity between the public behavior graph and the software behavior graph, and use it as the basis for detection. Our evaluation of the dataset containing 1274 malware apps and 108 benign apps shows that the selected public behavior graph is effective for Android malware detection, with an overall accuracy of 90.4%. The experimental results show that the method has a high detection rate and a low false rate, and can detect malware and its variants. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile telecommunication systems

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Scenario based intelligent desensitization and dynamic access control technology for power distribution and distributed new energy data resources 6

Scenario based intelligent desensitization and dynamic acces...

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6th International Conference on Intelligent computing, Communication, and Devices, ICCD 2023

作者： Min, Xu Aihua, Zhou Yiqing, Wang Xiaofeng, Shen Lin, Peng Nige, Li Gang, Wang He, Wang State Grid Smart Grid Research Institute Co. LTD. Jiangsu Nanjing210003 China State Grid Key Laboratory of Information and Network Security Nanjing210003 China State Grid Jiangsu Electric Power Co. Ltd Jiangsu Nanjing210024 China State Grid Shanghai Electric Power Company Shanghai200122 China

ISBN: (数字)9781510666269

ISBN: (纸本)9781510666252

The new power system has a new operation mode of "the source follows the load, the load follows the network, and the source loads interact". With the continuous excavation of flexible regulation resources, new third-party entities such as virtual power plants or load integrators need to consider implementing differentiated network security protection requirements to ensure their fair access to business. At the same time, with the clarity of the regulation level of the new power system and the continuous participation of various entities in the construction, the company's network security protection boundary will no longer be "horizontal and vertical", but will become more uneven, with more types of interfaces and interfaces. It is necessary to refine the boundary protection measures according to different interaction needs. The new regulatory subject on the demand side of the new power system involves the privacy information of a large number of users. Data flow and sharing, cross access and collaborative analysis among the subjects have security problems such as unauthorized access and privacy leakage. This paper focuses on the scenario based intelligent desensitization and dynamic access control technology for power distribution and distributed new energy data resources, and combs various factors involved in the data access process, On the basis of obtaining the data access authority, the dynamic correlation analysis of the three elements of user role and access authority, sensitive data level, power allocation and distributed new energy business scenario is integrated, and finally the appropriate desensitization method is selected from the desensitization algorithm library, and finally the appropriate desensitization method is selected from the desensitization algorithm library to ensure the security protection capability of the new distribution network under the flexible, open and interactive energy scenario, Effectively respond to new major network security ris

关键词： network security

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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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Continuous Verification of Catastrophic Recalling in Machine Unlearning via Adversarial Testing 9

Continuous Verification of Catastrophic Recalling in Machine...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Qu, Y. Neil Ji, Zonghao Cui, Lei Zhang, Chong Liu, Lei Tian, Zhiyi 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 Deakin University School of IT VIC3125 Australia Xidian University Guangzhou Institute of Technology Guangzhou510555 China University of Technology School of Computer Science SydneyNSW2007 Australia

ISBN: (纸本)9798350391367

Throughout the process of machine unlearning, in order to fulfill the requirements of machine unlearning and to comply with the rules of the right to be forgotten, it is natural to verify whether the unlearning model actually removes data points. Therefore, machine unlearning verification plays a crucial role. However, existing validation methods only perform one validation, and it is still challenging to determine whether subsequent updates of the model still maintain the unlearning effect. To address this challenge, we propose a novel continuous verification of catastrophic recalling in machine unlearning framework that aims to continuously evaluate whether a machine learning model effectively forgets specified data after an update. In this work, we first introduce adversarial testing as a crucial part of the overall framework. Verification then continues as the unlearned model is updated (adding or continuing to forget data points). We also analyzed the possibility that learning new data after unlearning may diminish the effect. Overall, our continuous unlearning verification framework provides a proactive approach to protecting individual privacy in the machine learning domain, thus facilitating the generation of more robust unlearning algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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network security Situational Awareness Model Based on Threat Intelligence 14th

Network Security Situational Awareness Model Based on Threat...

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14th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2021

作者： Zhang, Hongbin Yin, Yan Zhao, Dongmei Liu, Bin Gao, Hongbin School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science Shijiazhuang05000 China

ISBN: (纸本)9783030898137

In order to deal with the problems that the increasing scale of the network in the real environment leads to the continuous high incidence of network attacks, the threat intelligence was applied to situational awareness, and the situational awareness model based on random game was constructed. Threat perception of the target system was performed by comparing the similarity between the exogenous threat intelligence and the internal security events of the system. At the same time, internal threat intelligence was generated based on the threat information inside the system. In this process, game theory was used to quantify the current network security situation of the system, evaluate the security status of the network. Finally, the prediction of the network security situation was realized. The experimental results show that the network security situation awareness method based on threat intelligence can reflect the changes in the network security situation and predict attack behaviors accurately. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Game theory

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Enhance information Flow Tracking with Function Recognition

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China Communications 2010年第6期7卷 24-29页

作者： Zhou Kan Huang Shiqiu Huang Shan Qi Zhengwei Gu Jian Guan Haibing School of software Shanghai Jliao Tong University Shanghai 200240 P. R. China Key Lab of Information Network Security Ministry of Public Security Department of Computer Science ＆ Engineering Shanghai Jiao Tong University Shanghai 200240 P. R China Shanghai Key Laboratory of Scalable Computing ＆ System Shanghai P. R. China

With the spread use of the computers, a new crime space and method are presented for criminals. Thus computer evidence plays a key part in criminal cases. Traditional computer evidence searches require that the computer specialists know what is stored in the given computer. Binary-based information flow tracking which concerns the changes of control flow is an effective way to analyze the behavior of a program. The existing systems ignore the modifications of the data flow, which may be also a malicious behavior. Thus the function recognition is introduced to improve the information flow tracking. Function recognition is a helpful technique recognizing the function body from the software binary to analyze the binary code. And that no false positive and no false negative in our experiments strongly proves that our approach is effective.

关键词： function recognition information flow tracking program behavior e-evidence.

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Data-Driven Active power Dispatch for Wind Farms 13

Data-Driven Active Power Dispatch for Wind Farms

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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Jiao, Xuguo Zhang, Daoyuan Wang, Xin Ma, Zhaoxing Zhang, Zhenyong Liu, Wenfeng School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China School of Civil Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9798350361674

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method. © 2024 IEEE.

关键词： Cost functions

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Spatial-Spectral Transformer for Local and Global Hyperspectral Image Classification

Spatial-Spectral Transformer for Local and Global Hyperspect...

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Signal, information and Data Processing (ICSIDP), IEEE International Conference on

作者： MengXin Cao Guixin Zhao Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Deep learning techniques have become increasingly prevalent in hyperspectral image (HSI) classification. However, convolutional neural network (CNN)-based approaches often struggle with inefficient utilization of spectral sequence data and inadequate processing of fine-grained spectral details. In contrast, transformer models are inherently designed to capture sequence dependencies across spectral bands. Yet, most existing approaches tend to focus on either local or global feature extraction separately, which limits their ability to fully exploit the spectral information. To overcome these limitations, we propose the Local and Global Fusion Spectral Transformer (LGFST), an innovative architecture that combines Swin Transformer blocks, standard Transformer blocks, and a novel Local-Global Fusion (LGF) module. This architecture is designed to simultaneously capture both local and global features, thereby improving the extraction of detailed spectral information. The Swin Transformer block, integrated with patch merging and hierarchical attention mechanisms, expands the receptive field and enhances local feature representation through both intra- and cross-window interactions. Meanwhile, the Transformer block performs self-attention across all tokens to extract global features. The fusion of local and global features from these two blocks effectively improves the texture and discriminative power of spectral data, leading to enhanced classification performance, especially in scenarios with dense class distributions. Finally, the LGF module facilitates interaction between local and global feature sets, ensuring a seamless integration of information from both blocks. Extensive experiments on three benchmark HSI datasets demonstrate that LGFST outperforms conventional transformer models and sets new benchmarks in performance compared to state-of-the-art methods.

关键词： Merging Benchmark testing Transformers Feature extraction Distortion Sensors Data mining Standards Hyperspectral imaging Image classification

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A generalized reliability analysis of 3-ary n-cubes

A generalized reliability analysis of 3-ary n-cubes

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International Conference on Communication Software and networks, ICCSN

作者： Zefang Bai Guijuan Wang Meijie Ma School of Mathematics and Statistics Qilu University of Technology (Shandong Academy of Sciences) Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331506322

ISBN: (纸本)9798331506339

Evaluating the reliability of interconnection networks is crucial. The edge-connectivity is a parameter used to assess reliability. In this paper, we discuss a conditional edge-connectivity called the h-extra g-component edge-connectivity. It combines the extra edge-connectivity and the component edge-connectivity. First, this paper proved that $c \lambda_{3}^{h}\left(Q_{n}^{3}\right)=4 n h-$ $4 e_{h}\left(Q_{n}^{3}\right)-h$ for $1 \leq h \leq \frac{1}{2} \cdot 3^{\left\lceil\frac{h}{2}\right\rceil}, n \geq 3$. Then, the paper given the upper bound of h-extra g-component edge-connectivity of the 3 -ary n-cubes for $g \in\left\{4,3^{c}+1\right\}, 1 \leq c \leq\left\lceil\frac{n}{2}\right\rceil-1, n \geq 3$.

关键词： Upper bound Multiprocessor interconnection Software Reliability

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