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SCARE of Secret Ciphers under Rough Leakage Model

security and Communication networks 2023年第1期2023卷

作者： Wang, Mengxing Tang, Ming Zhang, Xige Chen, Qi Ding, Haili Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Hubei Wuhan430000 China Beijing Smartchip Microelectronics Technology Co. Ltd. Beijing100192 China Marketing Service Center State Grid Electric Power Co. Ltd. Ningxia750002 China

Most previousside-channel analysis-based reverse engineering (SCARE) methods were based on collision attack. However, the collision-based methods generally need noise-free traces and can hardly be verified by realistic implementations. This paper proposes a novel SCARE, which is named as OSHDG-SCARE, targeting on secret S-box. We define a graph as OSHDG (one-step HD graph) to recover the parameters of any secret S-box. Due to the redundancy of OSHDG, the multiple paths can filter the correct results and improve the success rate of the reverse method even when the samples deviate from leakage model. We classify the deviation of side channel signal as two types, which are the gap between real leakage and leakage model, and measurement noise. Experiments are performed on real power traces satisfying the inaccurate HW (Hamming Weight) leakage model from a software implementation of AES-like cipher. OSHDG-SCARE recovers the secret S-box when the deviation rate of traces is 1.45%. © 2023 Mengxing Wang et al.

关键词： Side channel attack

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Social-enhanced recommendation using graph-based contrastive learning

Social-enhanced recommendation using graph-based contrastive...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Peng Xue Qian Gao Jun Fan 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 Shandong China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Shandong China China Telecom Digital Intelligence Techonology Co Ltd Jinan Shandong China

The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation algorithms, the social neighbors' contributions are difficult to distinguish from the central user's, neglecting hidden correlations in social infor-mation. In order to solve this problem, this paper presents a graph-based contrastive learning framework for social-enhanced recommendation (SoGCLR) that utilizes implicit social information captured by a new social relation attention mechanism, en-rich user representations, and improve model robustness through graph-based contrastive learning. Specifically, the paper captures the degree of contribution of each neighbor in the social graph to the central user through a social relation attention layer, thus obtaining hidden correlations in social information, and further integrates this with user information in the user-item interaction graph to enrich user representations. In addition, the paper incorporates graph-based contrastive learning into the recommendation task using cross-layer contrastive learning, calculating contrastive loss and mapping nodes with similar but different exposure rates to nearby regions to mitigate exposure bias issues. Results from Ciao and Epinions demonstrate that SoGCLR reduces RMSE and MAE by 1.33% to 1.84% compared with baseline models.

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A Load-Balancing Algorithm for power Internet of Things Resources Based on the Improved Weighted Minimum Number of Connections 7th

A Load-Balancing Algorithm for Power Internet of Things Reso...

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7th International Conference on Artificial Intelligence and security, ICAIS 2021

作者： Zhang, Mingming Mao, Jiaming Chen, Lu Li, Nige Fan, Lei Shuang, Leshan State Grid Jiangsu Electric Power Co. Ltd. Information and Telecommunication Branch Nanjing Jiangsu210003 China Institute of Information and Communication Global Energy Interconnection Research Institute State Grid Key Laboratory of Information and Network Security Nanjing Jiangsu210003 China Broadband Wireless Communication Technology Engineering Research Center of the Ministry of Education Nanjing University of Posts and Telecommunications Nanjing Jiangsu210003 China

ISBN: (纸本)9783030786113

With the gradual increase of user visits in the power Internet of Things, load imbalances often appear, affecting server operation efficiency. This article is an improvement based on the weighted minimum number of connections algorithm. It sets weights for a group of servers and proposes two reference values: range and variance, and sets corresponding thresholds. Only the servers that exceed the threshold are used for the following operations. All data use re-hashing to deal with conflicts, and this process is repeated until an idle server is found to process the data. The simulation results show that the improved algorithm can effectively balance the user's task requests, realize the entire system's load balance, have good stability, and achieve the expected effect. © 2021, Springer Nature Switzerland AG.

关键词： Internet of things

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LI-FPN: Depression and Anxiety Detection from Learning and Imitation

LI-FPN: Depression and Anxiety Detection from Learning and I...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xingyun Li Lin Lu Xinyu Yi Hao Wang Yunshao Zheng Yanhong Yu Qingxiang Wang 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 Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Mental Health Center Jinan China Shandong University of Traditional Chinese Medicine Jinan China

With the rise in societal pressures, depression and anxiety have increasingly become prominent mental health conditions impacting people’s lives. To enhance the efficacy of automatic detection for these disorders, we have developed an experimental framework called the Voluntary Facial Expression Mimicry(VFEM). This framework led to the creation of the VFEM Dataset, which supports related research endeavors. Subsequently, we introduce the LI-FPN designed specifically for the automatic identification of depression and anxiety disorders. The LI-FPN comprises two core components: the Learning and Imitation Module(LIM) and the Spatio-temporal Feature Pyramid network(STFPN). Within the LIM, we leverage sequence features to facilitate comprehensive feature extraction through learning and imitation steps. The STFPN is designed to focus on outliers in multi-scale features for further screening. Compared with traditional attention methods, LI-FPN is more suitable for processing sequence data features and small sample datasets. Upon training using the VFEM Dataset, the LI-FPN achieves impressive accuracies: 0.850 for depression detection, 0.835 for anxiety detection, and 0.786 for co-occurrence detection of depression and anxiety. Meanwhile, LI-FPN also achieves SOAT results on AVEC2014 dataset. The source code for LI-FPN is accessible at https://***/muzixingyun/LI-FPN

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Auction Theory and Game Theory Based Pricing of Edge computing Resources: A Survey

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IEEE Internet of Things Journal 2025年

作者： Yu, Jiguo Liu, Shun Zou, Yifei Wang, Guijuan Hu, Chunqiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Qufu Normal University School of Computer Science Shandong Rizhao China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250353 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China

The proliferation of Internet of Things (IoT) devices and edge computing applications has heightened the demand for efficient resource allocation and pricing mechanisms. Effective pricing strategies play a crucial role in optimizing resource utilization, reducing latency, and ensuring fair resource distribution among users. Both auction theory and game theory have emerged as fundamental economic frameworks for designing pricing mechanisms in edge computing. This paper presents a comprehensive survey of pricing strategies that leverage both auction and game theory, systematically categorizing existing research based on auction mechanisms and game-theoretic models. Unlike previous surveys that focus solely on auction-based pricing, this work integrates game-theoretic perspectives, providing a holistic analysis of strategic interactions in edge computing resource allocation. Furthermore, we discuss key challenges, emerging trends, and future research directions aimed at enhancing efficiency, fairness, and privacy protection in cloud-edge collaborative computing environments. © 2014 IEEE.

关键词： Mobile edge computing

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External Knowledge Is Not Always Needed: An Adaptive Retrieval Augmented Generation Method

External Knowledge Is Not Always Needed: An Adaptive Retriev...

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Artificial Intelligence and Intelligent Manufacturing (AIIM), International Symposium on

作者： Wenbo Guan Jiyu Lu Qinyu Feng Xiaoqian Li Jun Zhou Key Laboratory of Speech Acoustics and Content Understanding Institute of Acoustics Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Institute of Information Engineering Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China China Southern Power Grid Artificial Intelligence Technology Co. Ltd Guangzhou China School of Computing Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

Retrieval augmented generation (RAG), by integrating external knowledge with large language models (LLMs), has become a common practice to alleviate LLMs’ hallucination problem. The performance of RAG, however, depends on the capability of the adopted information retriever to a large extent. Specifically, a good information retriever can manage to obtain the most useful information from the external knowledge, which will then enhance the quality of generated content from LLMs and vice versa. Since the vanilla RAG will always leverage the retrieved information to assist with LLMs’ content generation despite of its usefulness, when combined with a retriever of limited capacity, therefore, the vanilla RAG will not benefit LLMs’ content generation sometimes but introduce additional noise, having a negative impact on the final performance. To address this problem, this paper proposes a novel adaptive RAG method which first uses LLMs to determine the usefulness of the retrieved information and then let LLMs to generate content on their own without the retrieved information if it is useless. Experimental results on four datasets demonstrate the effectiveness of the proposed adaptive RAG method with information retrievers of various capabilities, improving the performance of the vanilla RAG by an obvious margin.

关键词： Large language models Retrieval augmented generation Noise Manufacturing

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Multi-Channel Hypergraph Convolution Group Recommendation with Member information Enhancement

Multi-Channel Hypergraph Convolution Group Recommendation wi...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Tianhao Chen Qian Gao Jun Fan 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 Shandong China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Shandong China China Telecom Digital Intelligence Techonology Co Ltd Jinan Shandong China

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach.

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Several constructions of optimal LCD codes over small finite fields

arXiv

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arXiv 2022年

作者： Li, Shitao Shi, Minjia Liu, Huizhou School of Mathematical Sciences Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China State Grid Anhui Electric Power Co. Ltd. Hefei China

Linear complementary dual (LCD) codes are linear codes which intersect their dual codes trivially, which have been of interest and extensively studied due to their practical applications in computational complexity and information protection. In this paper, we give some methods for constructing LCD codes over small finite fields by modifying some typical methods for constructing linear codes. We show that all odd-like binary LCD codes, ternary LCD codes and quaternary Hermitian LCD codes can be constructed using the modified methods. Our results improve the known lower bounds on the largest minimum distances of LCD codes. Furthermore, we give two counterexamples to disprove the conjecture proposed by Bouyuklieva (Des. Codes Cryptogr. 89(11): 2445-2461, 2021). Copyright © 2022, The Authors. All rights reserved.

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Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial network

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Computer Systems Science & Engineering 2021年第12期39卷 373-390页

作者： Jiaming Mao Mingming Zhang Mu Chen Lu Chen Fei Xia Lei Fan ZiXuan Wang Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information and Telecommunication BranchNanJing210003China Institute of Information and Communication Global Energy Interconnection Research InstituteState Grid Key Laboratory of Information and Network SecurityNanjing210003China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Broadband Wireless Communication Technology Engineering Research Center of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic *** the past few decades,network traffic identification has been a research hotspot in the field of network management and security ***,as more network services use encryption technology,network traffic identification faces many *** classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and *** learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled ***,in real scenarios,labeled samples are often difficult to *** paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised *** results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.

关键词： Encrypted traffic recognition deep learning generative adversarial network traffic classification semisupervised learning

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Wireless powered Backscatter Communications with Spatially Random Tags

Wireless Powered Backscatter Communications with Spatially R...

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IEEE International Conference on Communications in China (ICCC)

作者： Yingting Liu Yinghui Ye Xingwang Li Zhongyu Ma Zhiyang Zhou School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China State Grid Gansu Electric Power Research Institute Lanzhou China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts & Telecommunications Xi’an China School of Physics and Electronic Information Engineering Henan Polytechnic University Jiaozuo China College of Computer Science and Engineering Northwest Normal University Lanzhou China

In this paper, a wireless powered backscatter communication (BackCom) system is considered, which consists of one dedicated radio-frequency source node (S), one destination node and multiple tags randomly located in the coverage region of S. The consumed power by tags is considered and an optimal dynamic reflection coefficient (RC) is used to maximize the power of the backscattered signals while just satisfying the tag’s power requirement. The random tag selection (RTS) and nearest tag selection (NTS) schemes are proposed and the corresponding outage probabilities and diversity orders are derived. Numerical results verify the theoretical analysis and show that the NTS scheme has the best performance compared to the RTS and farthest tag selection schemes. Moreover, numerical results also show that the performance of the adopted optimal dynamic RC is superior to that of the fixed RC.

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