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An Efficient Restrictive Blind Signature Scheme with Applications to Electronic Cash

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High Technology Letters 2002年第4期8卷 60-63页

作者：谭运猛 Yang Zongkai Wu Shizhong Dept. of Electronics & information Eng. Huazhong University of Science and Technology Wuhan 430074 P.R.China national information security testing evaluation & certification center Beijing 100091 P.R.China

In this paper, a new restrictive blind signature scheme is proposed. Compared with Brands restrictive blind signature scheme, our scheme is even more restrictive and efficient. And our scheme is proved secure, too. A new withdrawal protocol of electronic cash system is designed by using our restrictive blind signature scheme, which is more efficient than the withdrawal protocol and is more appropriate for adopting pre processing and post processing.

关键词： restrictive blind signature scheme,applications to electronic

A Heuristic Algorithm on QoS Routing

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High Technology Letters 2002年第3期8卷 52-56页

This paper focuses on solving the delay constrained least cost routing problem, and propose a simple, distributed heuristic solution, called distributed recursive delay constrained least cost (DR DCLC) unicast routing algorithm. DR DCLC only requires local information to find the near optimal solution. The correctness of DR DCLC is proued by showing that it is always capable of constructing a loop free delay constrained path within finite time, if such a path exists. Simulation is also used to compare DR DCLC to the optimal DCLC algorithm and other algorithms.

关键词： routing quality of service distributed recursive algorithm delay constraints

Identifying Honeypots from ICS Devices Using Lightweight Fuzzy testing

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Computers, Materials & Continua 2020年第11期65卷 1723-1737页

作者： Yanbin Sun Xiaojun Pan Chao Xu Penggang Sun Quanlong Guan Mohan Li Men Han Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China Guangzhou information Technology security evaluation center Guangzhou510006China Jinan University Guangzhou510006China Kennesaw State University 1100 South Marietta Pkwy MariettaGeorgia30060USA

The security issues of industrial control systems(ICSs)have become increasingly *** an important part of ICS security,honeypots and anti-honeypots have become the focus of offensive and defensive ***,research on ICS honeypots still lacks breakthroughs,and it is difficult to simulate real ICS devices *** this paper,we studied ICS honeypots to identify and address their ***,an intelligent honeypot identification framework is proposed,based on which feature data type requirements and feature data acquisition for honeypot identification is *** by vulnerability mining,we propose a feature acquisition approach based on lightweight fuzz testing,which utilizes the differences in error handling between the ICS device and the ICS *** combining the proposed method with common feature acquisition approaches,the integrated feature data can be *** experimental results show that the feature data acquired is effective for honeypot identification.

关键词： ICS device honeypot identification

Software Defect Prediction via Deep Belief Network

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Chinese Journal of Electronics 2019年第5期28卷 925-932页

作者： WEI Hua SHAN Chun HU Changzhen ZHANG Yu YU Xiao School of Computer Science and Technology Beijing Institute of Technology china information Technology security evaluation center Beijing Key Laboratory of Software security Engineering Technology Beijing Institute of Technology School of Electrical and information Engineering and Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture State Key Laboratory in china for Geomechanics and Deep Underground Engineering (Beijing) China University of Mining and Technology School of Computer Science and Technology Shandong University of Technology

Defect distribution prediction is a meaningful topic because software defects are the fundamental cause of many attacks and data loss. Building accurate prediction models can help developers find bugs and prioritize their testing efforts. Previous researches focus on exploring different machine learning algorithms based on the features that encode the characteristics of *** problem of data redundancy exists in software defect data set, which has great influence on prediction *** propose a defect distribution prediction model(Deep belief network prediction model, DBNPM), a system for detecting whether a program module contains defects. The key insight of DBNPM is Deep belief network(DBN)technology, which is an effective deep learning technique in image processing and natural language processing,whose features are similar to defects in source *** results show that DBNPM can efficiently extract and process the data characteristics of source program and the performance is better than Support vector machine(SVM), Locally linear embedding SVM(LLE-SVM), and Neighborhood preserving embedding SVM(NPE-SVM).

关键词： Defect prediction Software security Deep belief network(DBN)

An Efficient Fair Electronic Cash System

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High Technology Letters 2003年第3期9卷 1-5页

作者：郭涛 Li Zhitang Tan Yunmeng Wu Shizhong Department of Computer Science & Technology Huazhong University of Science and Technology Wuhan 430074 P.R.China Department of Communication Science & Technology Huazhong University of Science and TechnologyWuhan 430074 P.R.China china national information security testing evaluation & certification center Beijing 100091P.R.China

In this paper, an efficient fair e-cash system is presented. Based on the improved Brands’ e-cash scheme, it is expanded by adding two roles, government and judges. The user can keep unconditionally anonymous in normal transactions. Authorized by the judges, the government can remove the identity of an illegal user with the help of the bank. So such misuse as blackmailing or money laundering can be prevented. Therefore, this scheme is more efficient, more suitable for adopting pre-processing and post-processing and more practical. In the paper, the details of the scheme are described, its security is proved, and its efficiency is analyzed.

关键词： e-cash electronic payment electronic commerce

Residential Energy Consumption Forecasting Based on Federated Reinforcement Learning with Data Privacy Protection

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 717-732页

作者： You Lu Linqian Cui YunzheWang Jiacheng Sun Lanhui Liu School of Electronic and information Engineering Suzhou University of Science and TechnologySuzhou215009China Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and TechnologySuzhou215009China Chongqing Industrial Big Data Innovation center Co. Ltd.Chongqing400707China

Most studies have conducted experiments on predicting energy consumption by integrating data formodel ***, the process of centralizing data can cause problems of data ***,many laws and regulationson data security and privacy have been enacted, making it difficult to centralize data, which can lead to a datasilo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning ***, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg)method is used to directly weight the model parameters on average, which may have an adverse effect on te ***, we propose the Federated Reinforcement Learning (FedRL) model, which consists of multiple userscollaboratively training the model. Each household trains a local model on local data. These local data neverleave the local area, and only the encrypted parameters are uploaded to the central server to participate in thesecure aggregation of the global model. We improve FedAvg by incorporating a Q-learning algorithm to assignweights to each locally uploaded local model. And the model has improved predictive performance. We validatethe performance of the FedRL model by testing it on a real-world dataset and compare the experimental results withother models. The performance of our proposed method in most of the evaluation metrics is improved comparedto both the centralized and distributed models.

关键词： Energy consumption forecasting federated learning data privacy protection Q-learning