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3rd International Conference on Electronic information Engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Xia, Yuchao Xue, Mutian Li, Wei Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China University of Electronic Science and Technology of China Chengdu China Sichuan University of Media and Communications Chengdu China

ISBN: (纸本)9781510680449

The rapid expansion and diversification of internet traffic pose a pressing demand for accurate classification of diverse application streams in network management and security monitoring. This paper introduces a deep learning-based method for precise classification of diverse internet application streams in network. By transforming raw traffic data into grayscale images and utilizing Convolutional Neural networks (CNNs) and the Swin-Transformer framework, critical information is extracted at both local and global feature levels. The core of this method is offering a visual representation of the structure and content of traffic data. This visual representation enables subsequent feature extraction and classification. Through experimentation across various applications including email, Skype, YouTube, Vimeo, FTP, and Facebook, the proposed method in traffic classification is demonstrated to be effective. This approach boosts traffic classification accuracy, offers innovation in network management and security monitoring, and holds vital importance in optimizing network performance and security. Additionally, it sets the stage for future research in networking data processing and security methodologies. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： network security

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Brainnetformer: Decoding Brain Cognitive States with Spatial-Temporal Cross Attention 48

Brainnetformer: Decoding Brain Cognitive States with Spatial...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Sheng, Leheng Wang, Wenhan Shi, Zhiyi Zhan, Jichao Kong, Youyong Southeast University Chien-Shiung Wu College China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing China

ISBN: (纸本)9781728163277

Learning about the cognitive state of the brain has always been a popular topic. Based on the fact that fluctuations of brain signals and functional connectome (FC) relate to specific human behaviors, deep learning based methods have shown promising results on the prediction of such behaviors by analyzing biological signals. Existing methods either model from static perspectives or apply spatial-temporal graph convolution to extract dynamic properties. However, the static information and dynamic information can reflect global brain activities and local brain activities respectively. Thus, we propose BrainNetFormer to incorporate both static and dynamic properties for human behavior prediction. To be specific, a spatial cross attention module and a temporal cross attention module are introduced for information fusion. In addition, since a specific behavior of subjects can be decomposed into a series of subtasks, we introduce a sub-task regularization loss to assist in training and empower the model to recognize subtasks at each moment. Experiments on the HCP-Task dataset demonstrate the superior performance of the proposed model. © 2023 IEEE.

关键词： brain states decoding cross attention functional MRI images Spatial-temporal graph convolution

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Data Dynamics Enabled Privacy-Preserving Public Batch Auditing in Cloud Storage

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Chinese Journal of Electronics 2025年第2期23卷 297-301页

作者： Zhengwei Ren Lina Wang Qianhong Wu Ruyi Deng School of Computer Wuhan University Wuhan China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Wuhan China

Many schemes have been present to tackle data integrity and retrievability in cloud storage. Few of existing schemes support data dynamics, public verification and protect data privacy simultaneously. We propose a public auditing scheme which enables privacy-preserving, data dynamics and batch auditing. A data updating information table is designed to record the status information of the data blocks and facilitate data dynamics. Homomorphic authenticator and random masking technologies are exploited to protect data privacy for data owners. The scheme employs a Trusted third party auditor (TTPA) to verify the data integrity without learning any information about the data content during the auditing process. The scheme also allows batch auditing so that TTPA can process multiple auditing requests simultaneously which greatly accelerates the auditing process. security and performance analysis show that our scheme is secure and feasible.

关键词： Cloud computing Data privacy Data integrity Medical services Performance analysis security Proposals Protection

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Demand Response Scheme: Achieving Sustainability in Distributed Data Processing 9

Demand Response Scheme: Achieving Sustainability in Distribu...

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

作者： Zhai, Xueying Peng, Yunfeng Guo, Xiuping Zhang, Liang Zhang, Wei University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Beijing University of Posts and Telecommunications School of Economics and Management Beijing China Guizhou Institute of Technology Guizhou Key Laboratory of Electric Power Big Data Guizhou China 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

ISBN: (纸本)9798350391367

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. Data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization. © 2024 IEEE.

关键词： Low emission

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Research on the Design of Government Data Governance Evaluation System under the Perspective of Digital Government 9

Research on the Design of Government Data Governance Evaluat...

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

作者： Xu, Zirui Sun, Jinyang Lu, Zhe Han, Delong Qilu University of Technology Shandong Academy of Sciences Information Research Institute Shandong Academy of Sciences Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd. Jinan China 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

ISBN: (纸本)9798350391367

In the context of the digital era, government data governance is a key factor in promoting the modernization of government governance, but there is a lack of a complete set of evaluation index system to standardize the content and scope of government data governance. This paper adopts the policy tool theory to code and analyze the 12 policies from the three policy tool dimensions of supply layer, demand layer, and environment layer, and proposes three first-level evaluation indicators of basic environment, actual effectiveness, and safeguard mechanism, on the basis of which the evaluation content is selected in a focused manner based on the integration of policy documents related to data governance. Meanwhile, a comprehensive, scientific and objective evaluation system of government data governance is constructed by adopting assessment means combining subjectivity and objectivity, and quantitative and qualitative. The system is designed to assess and guide the data governance practices of the domestic government, point out the direction for government departments at all levels to carry out data governance, and play an important role in promoting the transparency and efficiency of data management. © 2024 IEEE.

关键词： data governance digital government government information indicator system policy tools

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Code Vulnerability Detection via Nearest Neighbor Mechanism

Code Vulnerability Detection via Nearest Neighbor Mechanism

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Du, Qianjin Kuang, Xiaohui Zhao, Gang Department of Computer Science and Technology Tsinghua University China National Key Laboratory of Science and Technology on Information System Security China

Code vulnerability detection is a fundamental and challenging task in the software security field. Existing research works aim to learn semantic information from the source code by utilizing NLP technologies. However, in vulnerability detection tasks, some vulnerable samples are very similar to non-vulnerable samples, which are difficult to identify. To address this issue and improve detection performance, we introduce the k-nearest neighbor mechanism which retrieves multiple neighbor samples and utilizes label information of retrieved neighbor samples to provide help for model predictions. Besides, we use supervised contrastive learning to make the model learn the discriminative representation and ensure that label information of retrieved neighbor samples is as consistent as possible with the label information of testing samples. Extensive experiments show that our method can achieve obvious performance improvements compared to baseline models. © 2022 Association for Computational Linguistics.

关键词： Semantics

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Reversible Data Hiding in Encrypted Images based on Classic McEliece Cryptosystem

Reversible Data Hiding in Encrypted Images based on Classic ...

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Image Processing, computer Vision and Machine Learning (ICICML), International Conference on

作者： Zongbao Jiang Minqing Zhang Fuqiang Di Weina Dong Key Laboratory of Network and Information Security of PAP Engineering University of PAP Xi’an China

Aiming at the problems that the security of traditional data hiding algorithms is difficult to be guaranteed and the existing algorithms’ embedded volume is restricted under quantum operation attacks, a reversible data hiding algorithm in encrypted images based on Classic McEliece cryptosystem is proposed. The image pixels are first decomposed into bit planes to generate a data stream, and then encrypted in groups using the Classic McEliece cipher algorithm. In the encryption process, the fault tolerance mechanism in its encryption algorithm is utilized to select the appropriate modulation noise composition and the mapping relationship between the secret data, and the error pattern is modulated into noise to interfere with the encryption process, so as to realize the embedding of the secret data. Finally, the marked encrypted image is obtained by using the exclusive-or encryption and scrambling encryption processes. The experimental results show that the algorithm has the reversibility of carrier recovery. Compared with the existing algorithms, this algorithm’s embedded volume is advantageous, and its highest embedding rate is 4.43548 bpp, which realizes large-capacity data hiding while improving security.

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A Data security Protection Method for Deep Neural network Model Based on Mobility and Sharing 1

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Zhao, Xinjian Li, Qianmu Wang, Qi Chen, Shi Li, Tengfei Li, Nianzhe State Grid Jiangsu Information and Telecommunication Company Jiangsu Nanjing210024 China School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China State Grid Smart Grid Research Institute Co. Ltd. Jiangsu Nanjing210003 China State Grid Key Laboratory of Information and Network Security Jiangsu Nanjing210003 China

ISBN: (数字)9789819998937

ISBN: (纸本)9789819998920

With the rapid development of digital economy, numerous business scenarios, such as smart grid, energy network, intelligent transportation, etc., require the design and distribution of deep neural network (DNN) models, which typically use large amounts of data and computing resources for training. As a result, DNN models are now considered important assets, but at great risk of being stolen and distributed illegally. In response to the new risks and challenges brought by frequent data flow and sharing, watermarks have been introduced to protect the ownership of DNN models. Watermarks can be extracted in a relatively simple manner to declare ownership of a model. However, watermarks are vulnerable to attacks. In this work, we propose a novel label-based black-box watermark model protection algorithm. Inspired by new labels, we design a method to embed watermarks by adding new labels in the model, and to prevent watermarks from being forged, we use encryption algorithms to generate key samples. We conduct experiments on the VGG19 model using the CIFAR-10 dataset. Experimental results show that the method is robust to fine-tuning attacks, pruning attacks. Furthermore, our method does not affect the performance of deep neural network models. This method helps to solve scenarios such as "internal data circulation, external data sharing and multi-party data collaboration". © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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Oracles-Based Framework for Introducing Governance Entitlements Across DAOs 30th

Oracles-Based Framework for Introducing Governance Entitleme...

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30th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2024

作者： Xu, Yuting Chen, Lin Yu, Xiang Liu, Kui Guangdong Key Laboratory of Blockchain Security Guangzhou University Guangzhou University Guangzhou510006 China School of Electronics and Information Engineering Taizhou University Taizhou318000 China Guangzhou University Technology Center of Network and Modern Education Technology Guangzhou Guangdong510006 China

ISBN: (纸本)9783031829062

With the arose of digital era, Decentralized Autonomous Organization (DAO), as the primary representatives of blockchain decentralized application organizational forms, have consistently showcased their autonomous governances advantages of transparency and trustlessness. Governance is the core of DAOs, directly determines the development of the entire organization. As current DAO governance is limited by inefficiency, the mainstream approach in the industry is to improve governance efficiency by optimizing governance mechanisms and designing incentive mechanisms to increase member participation. However, these approaches do not solve the problem of sharing governance entitlements across different organizations in a scalable way. This limitation results in inefficiencies in governance. Therefore, this paper proposes a framework which introduces governance entitlements based on oracles for a DAO platform. The platform uses oracles to verify the user’s external identity and anchor it with the internal platform identity. It also collects external governance data to achieve cross-organizational governance entitlements authentication, thus improving governance efficiency. Furthermore, we perform a formal analysis of the cross-chain authentication mechanism to demonstrate its security. Due to the differences in the governance entitlements of different DAOs, it lacks fairness to directly share the external governance entitlements of users. Therefore, this paper proposes a model for calculating governance equity that converts external equity classification into internal governance entitlements to ensure fairness in governance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Efficiency

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Persuasion Should be Double-Blind: A Multi-Domain Dialogue Dataset With Faithfulness Based on Causal Theory of Mind

arXiv

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arXiv 2025年

作者： Zhang, Dingyi Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

Persuasive dialogue plays a pivotal role in human communication, influencing various domains. Recent persuasive dialogue datasets often fail to align with real-world interpersonal interactions, leading to unfaithful representations. For instance, unrealistic scenarios may arise, such as when the persuadee explicitly instructs the persuader on which persuasion strategies to employ, with each of the persuadee’s questions corresponding to a specific strategy for the persuader to follow. This issue can be attributed to a violation of the "Double Blind" condition, where critical information is fully shared between participants. In actual human interactions, however, key information—such as the mental state of the persuadee and the persuasion strategies of the persuader—is not directly accessible. The persuader must infer the persuadee’s mental state using Theory of Mind capabilities and construct arguments that align with the persuadee’s motivations. To address this gap, we introduce ToMMA, a novel multi-agent framework for dialogue generation that is guided by causal Theory of Mind. This framework ensures that information remains undisclosed between agents, preserving "double-blind" conditions, while causal ToM directs the persuader’s reasoning, enhancing alignment with human-like persuasion dynamics. Consequently, we present CToMPersu, a multi-domain, multi-turn persuasive dialogue dataset that tackles both double-blind and logical coherence issues, demonstrating superior performance across multiple metrics and achieving better alignment with real human dialogues. Our dataset and prompts are available at https://***/DingyiZhang/ToMMA-CToMPersu. Copyright © 2025, The Authors. All rights reserved.

关键词： Economic and social effects

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