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检索条件"机构=Key Laboratory of Cyberspace and Data Security"

共 130 条记录，以下是41-50 订阅

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Research on the Parallel SM4-OFB Acceleration Based on Multiple Rounds of Iteration 3

Research on the Parallel SM4-OFB Acceleration Based on Multi...

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3rd International Conference on Artificial Intelligence and Computer Information Technology, AICIT 2024

作者： Han, Ya Wang, Mingsheng Li, Yongqiang Ren, Lifeng Institute of Information Engineering CAS Key Laboratory of Cyberspace Security Defense Beijing China Chongqing Research Institute of Big Data Peking University Chongqing China

ISBN: (纸本)9798350365443

In OFB mode, the output of the cryptographic algorithm is fed back to the input of the cryptographic algorithm. The OFB mode does not directly encrypt plaintext through cryptographic algorithms, but generates ciphertext blocks by XOR combining plaintext blocks with the output of cryptographic algorithms. In OFB encryption mode, messages are treated as bitstreams, and the output of block encryption is added to the encrypted messages, making it difficult for bit errors to propagate. The SM4 algorithm is a symmetric cryptographic technique developed by the Chinese National Cryptographic Administration and standardized as part of the State Encryption Standard in China. SM4 is widely adopted in various applications, including wireless communication systems and secure data transmissions. In this paper, a fast implementation method of SM4 in OFB mode is proposed. We calculate the possible parallelization operations in OFB mode using the operational relationship between the SM4 algorithm round functions, and ensure the correctness of SM4-OFB encryption through a feedback compensation method. © 2024 IEEE.

关键词： Encryption algorithms

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Blockchain Private key Storage Algorithm Based on Image Information Hiding 5th

Blockchain Private Key Storage Algorithm Based on Image Info...

引用

5th International Conference on Artificial Intelligence and security, ICAIS 2019

作者： Wang, Ning Chen, Yuling Yang, Yu Fang, Zigang Sun, Yizhe School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Public Big Data GuiZhou University GuiyangGuizhou China

ISBN: (纸本)9783030242671

The private key is the unique credential of the blockchain asset and can be used to sign and authenticate transactions. Losing the private key also loses the control of the account, so the leakage of the private key is one of the major factors affecting the security of the blockchain. In order to improve the security of blockchain private key storage technology in transactions, this paper proposes a blockchain private key storage algorithm based on image information hiding. In order to improve the camouflage ability and reduce the bit error rate, the algorithm adds random numbers and error correction code to the original private key to provide double protection. After generating the watermark information, the watermark information is embedded into the digital image by means of discrete wavelet transform and quantized index modulation to realize the process of information hiding. Finally, it is proved that this algorithm has good transparency and robustness, can hide the private key information visually, and extract it accurately. © 2019, Springer Nature Switzerland AG.

关键词： Blockchain

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PPCSB: A Privacy-Preserving Electricity Consumption Statistics and Billing Scheme in Smart Grid 5th

PPCSB: A Privacy-Preserving Electricity Consumption Statisti...

引用

5th International Conference on Artificial Intelligence and security, ICAIS 2019

作者： Li, Chenyang Chen, Yuling Yang, Yu Li, Chaochao Zeng, Yang School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Public Big Data GuiZhou University GuiyangGuizhou China

ISBN: (纸本)9783030242671

Research on the privacy protection of smart grids mostly stays in fixed electricity prices or electricity aggregation. Aiming at the problem of real-time pricing and privacy data protection of smart grid, this paper proposes a privacy-preserving electricity consumption statistics and billing scheme in smart grid (PPCSB). In this scheme, the techniques of additive homomorphic encryption and mixed multiplicative homomorphic encryption are used to ensure the security of the data communication;Batch verification is used to improve the efficiency of signature verification. In addition, the proposed scheme does not require a trusted third party in order to improve the usability of the scheme. Through performance analysis of the scheme, it shows that the scheme has better security and better functions. © 2019, Springer Nature Switzerland AG.

关键词： Smart power grids

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Heuristic-Q: A Privacy data Pricing Method Based on Heuristic Reinforcement Learning 5th

Heuristic-Q: A Privacy Data Pricing Method Based on Heuristi...

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5th International Conference on Artificial Intelligence and security, ICAIS 2019

作者： Chen, Xi Chen, Jingjie Chen, Yuling Yang, Jinglan Li, Deyin School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Public Big Data Guizhou University GuiyangGuizhou China

ISBN: (纸本)9783030242671

With the development of big data applications in recent years, the value of personal data has received more and more attention. How to balance the conflict between data value development and personal privacy protection is an urgent problem to be solved. In response to the above contradictions, the market proposed a private data transaction mechanism to realize the commercial use of private data. However, existing private data transaction mechanisms still have deficiencies in some respects. In order to seek pricing strategies that maximize total revenue, this paper designs and constructs a privacy data dynamic pricing model from the perspective of data collectors, introduces heuristic reinforcement learning ideas, and proposes a pricing strategy algorithm based on heuristic functions. Finally, the simulation experiment of the proposed pricing model and strategy learning algorithm is carried out, and its performance is analyzed. The simulation results confirm that the algorithm can help data collectors get higher returns in the process of limited private data transactions. © 2019, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Decode the Dark Side of the Language: Applications of LLMs in the Dark Web 9

Decode the Dark Side of the Language: Applications of LLMs i...

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9th IEEE International Conference on data Science in cyberspace, DSC 2024

作者： Chen, Hongfan Diao, Yigang Xiang, Hui Huo, Yixuan Xie, Xiaqing Zhao, Jiapeng Wang, Xuebin Sun, Yanwei Shi, Jinqiao Beijing University of Posts and Telecommunications School of Cyberspace Security Beijing China Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Beijing China Data and Technology Support Center Cyberspace Administration of China Beijing China Chinese Academy of Sciences Institute of Information Engineering Beijing China

ISBN: (纸本)9798350391367

Previous studies have discovered that Transformers pre-trained on large scale datasets have a great advantage over traditional methods and can achieve human-level performance in many NLP tasks. Various encoder-only models like BERT and RoBERTa are used in the researches of the language in the Dark Web and most of them have achieved state-of-the-art performance. However, decoder-only models are not frequently used as they were considered to be unsuitable for NLU tasks until recent advancements in large language models. In this paper, we discuss the application of large scale decoder-only models in NLP tasks of real-world Dark Web research. We explore the capabilities of these models with the most realistic datasets in this specific domain. Surprisingly, results show that decoder-only models can achieve similar or better performance compared with state-of-the-art encoder-only model in some cases and show their own unique advantages in such field. We also discuss the limitations of these models and the challenges associated with their application in such field. Our study unveils the potential of decoder-only models in NLP tasks of real-world Dark Web research. © 2024 IEEE.

关键词： Decoding

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Locally Private Estimator for Degree Distribution with Laplacian Mechanism 10

Locally Private Estimator for Degree Distribution with Lapla...

引用

10th IEEE Smart World Congress, SWC 2024

作者： Li, Jiayu Hu, Yuke Li, Xiaoguang Zhou, Shiqi Wang, Yuxiang Li, Fenghua Niu, Ben Institute of Information Engineering Chinese Academy of Sciences China University of Chinese Academy of Sciences School of Cyber Security China Key Laboratory of Cyberspace Security Defense China Zhejiang University The State Key Laboratory of Blockchain and Data Security China Xidian University School of Cyber Engineering China

ISBN: (纸本)9798331520861

Local differential privacy (LDP) provides a solution such that an untrusted server can achieve privacy-preserving data analysis. Node degree is one of the most fundamental metrics in graph data analysis. Existing LDP-based methods can not balance degree distribution estimation and degree distribution characteristic estimation. In this paper, we propose a local private estimator for degree distribution with Laplacian mechanism (LEDL), which uses elastic net regression to estimate the degree distribution, selects the regularization weight to prevent overfitting or underfitting. We can aslo adopt the existing method to calculate degree distribution characteristics according to data with Laplacian noise. Experiments demonstrate the effectiveness of LEDL in degree distribution and Laplacian mechanism in degree distribution characteristic estimation, six metrics are applied across four datasets. © 2024 IEEE.

关键词： Differential privacy

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A MIML-LSTM neural network for integrated fine-grained event forecasting 18

A MIML-LSTM neural network for integrated fine-grained event...

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2018 International Conference on Big data Technologies, ICBDT 2018 and its Workshop, 2018 2nd International Conference on Business Information Systems, ICBIS 2018

作者： Xue, Cong Zeng, Zehua He, Yuanye Wang, Lei Gao, Neng State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences China Data Assurance and Communication Security Research Center Chinese Academy of Sciences China School of Cyberspace Security University of Chinese Academy of Sciences China

ISBN: (纸本)9781450364270

Societal event forecasting plays a significant role in crisis warning and emergency management. Most traditional prediction methods focus on predicting whether specific events would happen or not. However, the results of these methods are not always informative for the policy makers due to excessive frequency, lack of details and supportive evidence about the predictive events. In this paper, we focus on the problem of integrated fine-grained event forecasting which is to predict the attributes of events and find out related precursors. Given a collection of news sequences, we transform the problem into a Multi-Instance Multi-Label learning (MIML) framework. Considering the sequential influence of events and hybridity of news, we implement the MIML framework based on the Long Short-Term Memory (LSTM) neural network, and propose the model called MIML-LSTM to extract three levels of deep features which represent news article, daily status and news sequence respectively. Based on this hierarchical representation, we design a compositional objective function for joint training of each part. Taking multiple types of protest event prediction as a demonstration, we evaluate the proposed model on news streams from three countries in Latin America, and the experimental results show the effectiveness of our model on integrated fine-grained event forecasting. © 2018 Copyright is held by the owner/author(s).

关键词： Long short-term memory

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Secure YOLOv3-SPP: Edge-Cooperative Privacy-preserving Object Detection for Connected Autonomous Vehicles

Secure YOLOv3-SPP: Edge-Cooperative Privacy-preserving Objec...

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Zhou, Yongjie Xiong, Jinbo Bi, Renwan Tian, Youliang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Fujian Provincial Key Laboratory of Network System Information Security College of Computer and Cyberspace Security Fujian Normal University FJ Fuzhou350117 China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

The connected autonomous vehicles (CAVs) are a key component of intelligent transportation systems (ITS) where vehicles communicate with each other to exchange sensing data from on-board sensors (e.g., high-definition cameras and lidar). For the sake of the category and location privacy leakage of sensing images shared by CAVs and computational inefficiency of privacy-preserving object detection framework in edge computing environment, we propose a lightweight and privacy-preserving detection framework (PPDF) to support secure extraction, classification and detection of image features, and achieve the goal of data transmission and computing security under the collaborative detection process of edge nodes. Particularly, we design a secure clustering prediction protocol of object anchors, a secure object classification and regression protocol, secure object upsampling and secure feature fusion protocols. Finally, PPDF based on edge-cooperation was constructed and two non-collusive edge servers were used to perform PPDF. Theoretical analysis of correctness, security and complexity indicate that PPDF can not only realize correctness of object detection, but also let category and location privacy of images are protected effectively and have excellent accuracy. Actual performance evaluation shows that PPDF can achieve the same detection accuracy as original YOLOv3-SPP model. At the same time, compared with homomorphic encryption and multi-round iterative calculation schemes, PPDF has obvious advantages in terms of computational cost and communication overhead. © 2022 IEEE.

关键词： Autonomous vehicles

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Which DGA family does a malicious domain name belong to 5

Which DGA family does a malicious domain name belong to

引用

5th IEEE International Conference on data Science in cyberspace, DSC 2020

作者： Zhang, Yunyi Wu, Yuelong Jin, Shuyuan Yat-sen University School of Data and Computer Science Sun Guangzhou China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen China GuangDong Key Laboratory of Information Security Technology Sun Yat-set University Guangzhou China

ISBN: (纸本)9781728195582

The Domain Generation Algorithm (DGA) is a technology that generates a large amount of domains in a short time, commonly applied to malware by malicious attackers to circumvent the security mechanisms, such as domain blacklist. Besides discovering DGA domains, identifying DGA families also is significant for detecting and analyzing malware, which provides security professionals with the perspective of comprehensive analysis. In this paper, we investigate 22 different DGA families and propose an effective approach to portray and classify DGA families, which utilizes the strong host association and family portrait to identify different DGA families among massive DGA domains. The approach mitigates the hurdle caused by the nearly 100 times data difference among different families, implementing DGA family clustering. The experimental results show that the proposed approach identifies all of the DGA families accurately in the network that contains six families. © 2020 IEEE.

关键词： Malware

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Anomaly detection mechanism based on hierarchical weights through large-scale log data

Anomaly detection mechanism based on hierarchical weights th...

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2021 International Conference on Computer Communication and Artificial Intelligence, CCAI 2021

作者： Xi, Lanlan Xin, Yang Luo, Shoushan Shang, Yanlei Tang, Qifeng University of Posts and Telecommunications School of Beijing Institute of Cyberspace Security Beijing China University of Postsand Telecommunications School of Beijing Institute of Cyberspace Security Beijing China Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Beijing China National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai China China-Europe Digital Economy Expert Group Shanghai China

ISBN: (纸本)9781728194011

In order to realize Intelligent Disaster Recovery and break the traditional reactive backup mode, it is necessary to forecast the potential system anomalies, and proactively backup the real-time datas and configurations. System logs record the running status as well as the critical events (including errors and warnings), which can help to detect system performance, debug system faults and analyze the causes of anomalies. What's more, with the features of real-time, hierarchies and easy-access, log data can be an ideal source for monitoring system status. To reduce the complexity and improve the robustness and practicability of existing log-based anomaly detection methods, we propose a new anomaly detection mechanism based on hierarchical weights, which can deal with unstable log data. We firstly extract semantic information of log strings, and get the word-level weights by SIF algorithm to embed log strings into vectors, which are then feed into attention-based Long Short-Term Memory(LSTM) deep learning network model. In addition to get sentence-level weight which can be used to explore the interdependence between different log sequences and improve the accuracy, we utilize attention weights to help with building workflow to diagnose the abnormal points in the execution of a specific task. Our experimental results show that the hierarchical weights mechanism can effectively improve accuracy of perdition task and reduce complexity of the model, which provides the feasibility foundation support for Intelligent Disaster Recovery. © 2021 IEEE.

关键词： Anomaly detection

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