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arXiv 2024年

作者： Huang, Hui Qu, Yingqi Liu, Jing Yang, Muyun Xu, Bing Zhao, Tiejun Lu, Wenpeng Faculty of Computing Harbin Institute of Technology Harbin China Baidu Inc. Beijing China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China

The proliferation of open-source Large Language Models (LLMs) underscores the pressing need for evaluation methods. Existing works primarily rely on external evaluators, focusing on training and prompting strategies. However, a crucial aspect – model-aware glass-box features – is overlooked. In this study, we explore the utility of glass-box features under the scenario of self-evaluation, namely applying an LLM to evaluate its own output. We investigate various glass-box feature groups and discovered that the softmax distribution serves as a reliable quality indicator for self-evaluation. Experimental results on public benchmarks validate the feasibility of self-evaluation of LLMs using glass-box features1 Copyright © 2024, The Authors. All rights reserved.

关键词： Open systems

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Risk Assessment Method of power Monitoring System Vulnerability Based on Attack Graph 14th

Risk Assessment Method of Power Monitoring System Vulnerabil...

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14th International Conference on Genetic and Evolutionary computing, ICGEC 2021

作者： Ye, Hongbo Qian, Kexiang Jin, Minghui Han, Xiao Xiao, Fei State Grid Shanghai Municipal Electric Power Company Shanghai China State Grid Key Laboratory of Information and Network Security Institute of Information and Telecommunication Global Energy Interconnection Research Institute Beijing China

ISBN: (纸本)9789811684296

In recent years, incidents threatening the network security of the power system industry have become more frequent, hackers’ attack methods have become more and more complex, and the difficulty of power system network security protection has continued to increase. Aiming at the problems of complex attack strategies and irrational attackers in the actual power system environment, a vulnerability risk assessment method for power monitoring systems based on attack graphs is proposed. First, according to the dependency between the vulnerabilities, based on the Common Vulnerability Scoring System (CVSS) framework of industrial control vulnerabilities, the attack path and attack graph model are established, and then the possibility of the vulnerability being exploited and the overall impact of the exploit are calculated. The damage degree of the power system network, and on this basis, the risk value of the vulnerability is calculated. The vulnerability risk calculated by this method can accurately reflect the impact of the vulnerability on the entire power system network. The experimental results show that the vulnerability risk assessment method of power monitoring system based on attack graph proposed in this paper can effectively quantitatively evaluate industrial control vulnerabilities and improve the level of industrial control security protection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： network security

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Large-Scale network Adaptive Situation Awareness Method in Spatio-Temporal Dimension 1

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1st International Artificial Intelligence Conference, IAIC 2023

作者： Zhang, Hongbin Xu, Ying Liu, Bin Zhao, Dongmei Bai, Yikang School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 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 Shijiazhuang China

ISBN: (数字)9789819713325

ISBN: (纸本)9789819713318

In large-scale networks, the state space is exploding and changing dynamically. This leads to difficulties in collecting and analyzing situational awareness data, so we construct an adaptive situational awareness model in spatio-temporal dimensions. In the spatial dimension, vulnerabilities’s threats are assessed through attack graphs combined with Shapley values. At the same time, vulnerability threats are dynamically quantified by updating the status node reachability probability in real time. In the temporal dimension, a game model is established by analyzing vulnerability attack graph nodes to dynamically adjust the observation frequency of high-risk vulnerabilities, focusing on the safety status characteristics of high-risk assets. Experimental results show that our proposal integrates the security features of both space and time dimensions. This method can better focus on high-risk vulnerabilities and accurately reflect the dynamic changes in the network security situation, ensuring timeliness and accuracy in network security detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： network security

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A Lightweight Node Verification and Protection Strategy for Edge computing

A Lightweight Node Verification and Protection Strategy for ...

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Artificial Intelligence, networking and information Technology (AINIT), International Seminar on

作者： Jiayuan Du Guowei Zhang School of Cyber Science and Engineering Qufu Normal University Qufu China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

To alleviate the burden on the core network, edge computing has rapidly advanced to support distributed computing. However, as sensitive information is delegated to the edge layer closer to endpoints, the privacy protection challenges faced by edge nodes intensify while striving to deliver high-quality services. Existing studies predominantly rely on various encryption algorithms to safeguard privacy information, such as identity, location, and offloading routes privacy. Unfortunately, these approaches exhibit high overhead and often neglect the potential maliciousness of the nodes themselves. To address these issues, this paper introduces a lightweight edge node verification and protection strategy. This strategy not only verifies the reliability of edge nodes but also enables anonymized uploading of private information. Numerical simulation results demonstrate the superiority of this strategy over existing schemes, showcasing reduced computational overhead while achieving enhanced protection.

关键词： Seminars Privacy Numerical simulation Mathematical models Performance analysis security Reliability

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network security Situation Assessment Method Based on Absorbing Markov Chain

Network Security Situation Assessment Method Based on Absorb...

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2022 International Conference on networking and network Applications, NaNA 2022

作者： Gao, Hongbin Wang, Shangxing Zhang, Hongbin Liu, Bin Zhao, Dongmei Liu, Zhen Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China Hebei University of Science and Technology School of Information Science and Engineering Hebei Key Laboratory of Network and Information Security Shijiazhuang China Hebei University of Science and Technology School of Economics and Management Research Center of Big Data and Social Computing Shijiazhuang China Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

This paper has a new network security evaluation method as an absorbing Markov chain-based assessment method. This method is different from other network security situation assessment methods based on graph theory. It effectively refinement issues such as poor objectivity of other methods, incomplete consideration of evaluation factors, and mismatching of evaluation results with the actual situation of the network. Firstly, this method collects the security elements in the network. Then, using graph theory combined with absorbing Markov chain, the threat values of vulnerable nodes are calculated and sorted. Finally, the maximum possible attack path is obtained by blending network asset information to determine the current network security status. The experimental results prove that the method fully considers the vulnerability and threat node ranking and the specific case of system network assets, which makes the evaluation result close to the actual network situation. © 2022 IEEE.

关键词： network security

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Cooperative structure of Li/Ni mixing and stacking faults for achieving high-capacity Co-free Li-rich oxides

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Journal of Energy Chemistry 2024年第8期95卷 315-324,I0007页

作者： Zhen Wu Yu-Han Zhang Hao Wang Zewen Liu Xudong Zhang Xin Dai Kunyang Zou Xiaoming Lou Xuechen Hu Lijing Ma Yan Liu Yongning Liu State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina Qingdao Industrial Energy Storage Research Institute Qingdao Institute of Bioenergy and Bioprocess TechnologyChinese Academy of SciencesQingdao 266101ShandongChina Hefei Advanced Computing Center Operation Management Corp Ltd Hefei 230088AnhuiChina Center for High Performance Computing Network Information CenterXi’an Jiaotong UniversityXi’an 710049ShaanxiChina Wuhan Dongfeng Motor Corporation Ltd Wuhan 430056HubeiChina School of Materials Science and Intelligent Engineering Nanjing UniversitySuzhou 215163JiangsuChina Key Laboratory of MEMS of the Ministry of Education Southeast UniversityNanjing 210096JiangsuChina International Research Center for Renewable Energy State Key Laboratory of Multiphase Flow in Power Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina

Co-free Li-rich layered oxides(LLOs)are emerging as promising cathode materials for Li-ion batteries due to their low cost and high ***,they commonly face severe structural instability and poor electrochemical activity,leading to diminished capacity and voltage ***,we introduce a Co-free LLO,Li_(1.167)Ni_(0.222)Mn_(0.611)O_(2)(Cf-L1),which features a cooperative structure of Li/Ni mixing and stacking *** structure regulates the crystal and electronic structures,resulting in a higher discharge capacity of 300.6 mA h g^(-1)and enhanced rate capability compared to the typical Co-free LLO,Li_(1.2)Ni_(0.2)Mn_(0.6)O_(2)(Cf-Ls).Density functional theory(DFT)indicates that Li/Ni mixing in LLOs leads to increased Li-O-Li configurations and higher anionic redox activities,while stacking faults further optimize the electronic interactions of transition metal(TM)3d and non-bonding O 2p ***,stacking faults accommodate lattice strain,improving electrochemical reversibility during charge/discharge cycles,as demonstrated by the in situ XRD of Cf-L1 showing less lattice evolution than *** study offers a structured approach to developing Co-free LLOs with enhanced capacity,voltage,rate capability,and cyclability,significantly impacting the advancement of the next-generation Li-ion batteries.

关键词： Co-free Li-rich oxides Li/Ni mixing Stacking faults Electronic structure

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Research on Cross-site Scripting Vulnerability of XSS Based on International Student Website 5

Research on Cross-site Scripting Vulnerability of XSS Based ...

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5th International Conference on Computer network, Electronic and Automation, ICCNEA 2022

作者： Wang, Tianma Zhao, Dongmei Qi, Jianjun International Education Institute North China Electric Power University Beijing China Hebei Normal University College of Computer and Cyber Security Shijiazhuang China Key Laboratory of Network and Information Security in Hebei Province Shijiazhuang China

ISBN: (数字)9781665491099

ISBN: (纸本)9781665491099

The international student website is the window of the school's external publicity. Through the website, international students can understand the school in a wider range and in all aspects. The website is often attacked by network hackers. How to ensure the security of the website? Cross site scripting attack (XSS) is the most common web application security vulnerability. Through the analysis of cross site scripting attack (XSS), this paper puts forward the preventive methods for input verification, output coding, clearly specifying the coding mode of output, paying attention to the limitations of blacklist verification mode, being alert to standardization errors, and gives examples of not inserting untrusted data in the allowed position, and HTML coding these data when inserting untrusted data between HTML tags, Before inserting untrusted data into common HTML attributes, encode HTML attributes;before inserting untrusted data into HTML JavaScript, encode JavaScript;before inserting untrusted data into HTML style attribute values, encode CSS;before inserting untrusted data into HTML URL attributes, encode URL;when using rich text, use XSS rule engine to encode and filter and other corresponding defense rules. © 2022 IEEE.

关键词： Websites

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Towards an Ontology of Traceable Impact Management in the Food Supply Chain

arXiv

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arXiv 2025年

作者： Gajderowicz, Bart Fox, Mark S. Gao, Yongchao Urban Data Centre School of Cities University of Toronto Toronto Canada Key Laboratory of Computing Power Network and Information Security Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China

The pursuit of quality improvements and accountability in the food supply chains, especially how they relate to food-related outcomes, such as hunger, has become increasingly vital, necessitating a comprehensive approach that encompasses product quality and its impact on various stakeholders and their communities. Such an approach offers numerous benefits in increasing product quality and eliminating superfluous measurements while appraising and alleviating the broader societal and environmental repercussions. A traceable impact management model (TIMM) provides an impact structure and a reporting mechanism that identifies each stakeholder’s role in the total impact of food production and consumption stages. The model aims to increase traceability’s utility in understanding the impact of changes on communities affected by food production and consumption, aligning with current and future government requirements, and addressing the needs of communities and consumers. This holistic approach is further supported by an ontological model that forms the logical foundation and a unified terminology. By proposing a holistic and integrated solution across multiple stakeholders, the model emphasizes quality and the extensive impact of championing accountability, sustainability, and responsible practices with global traceability. With these combined efforts, the food supply chain moves toward a global tracking and tracing process that not only ensures product quality but also addresses its impact on a broader scale, fostering accountability, sustainability, and responsible food production and consumption. © 2025, CC BY.

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Long and Recent Preference Learning with Recent-k Items Distribution for Recommender System

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IEEE Transactions on Multimedia 2025年

作者： Gao, Yongbiao Niu, Sijie Lv, Guohua Ling, Miaogen Geng, Xin Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China Southeast University Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing School of Information Science and Engineering China Nanjing University of Information Science and Technology School of Computer Science China Southeast University School of Computer Science and Engineering China

Reinforcement learning (RL) aims to formulate the recommendation task as a Markov decision process (MDP) and trains an agent to automatically learn the optimal recommendation policy from interaction trajectories through trial-and-error and reward mechanisms. However, most existing RL-based approaches overlook the correlation between items and the dynamics of user interests implied in temporally close interactions. Therefore, in this paper, we propose a reinforcement learning method that incorporates a "recent-k items"distribution to capture users' local preferences. Specifically, we model the output layer as two distinct branches. The "recent-k items"branch, formulated with a Kullback-Leibler divergence loss, learns the recent interests of users, whereas the other branch utilizes a one-step temporal difference error to capture long-term preferences. The proposed structure is integrated into deep Q-learning and actor-critics, resulting in two enhanced methods named R k Q and R k AC, respectively. Furthermore, a novel soft inter-reward is carefully designed to enhance the proposed method, and we theoretically prove the convergence of the proposed algorithm. We perform extensive experiments on two large real-world datasets and conduct further analysis of the influences of different action sequences, time intervals, and enhancement capabilities for state-of-the-art models. The experimental results demonstrate the efficacy of our proposed methods. © 1999-2012 IEEE.

关键词： Deep reinforcement learning

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A Web Attack Detection Method Based on DistilBERT and Feature Fusion for power Micro-Application Server 2

A Web Attack Detection Method Based on DistilBERT and Featur...

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2nd International Conference on Advanced Electronics, Electrical and Green Energy, AEEGE 2023

作者： Nige, Li Lu, Chen Lei, Zhao Zhenning, Teng Zhiqiang, Wang Yiyang, Shao Xiaolin, Gui State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co.Ltd. Nanjing China Xi'an Jiaotong University Faculty of Electronic and Information Engineering Shaanxi Province Key Laboratory of Computer Network Xi'an China

ISBN: (纸本)9798350322866

The widespread use of power micro-applications has significantly expanded the attack surface of their server side, thus increasing the risk of vulnerability attacks such as SQL injection, XSS, and CRLF injection. To ensure the security of power systems, these attacks must be detected precisely and timely. Therefore, this paper proposes a server-side web attack detection method based on DistilBERT and feature fusion for HTTP and HTTPS requests. The method treats HTTP and HTTPS requests as text data, specifically, extracts deep semantic features using DistilBERT, and fuses them with well-designed empirical features to comprehensively characterize and classify the request. Consequently, anomalous requests can be detected. The experimental results show that the accuracy, precision, recall, and F1 score of the method on HTTP CSIC 2010 and FWAF datasets are close to or higher than 99%. Compared with other methods as far as we known, this method has better performance and efficiency. © 2023 IEEE.

关键词： Cybersecurity

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