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6th IEEE International Conference on Data Science in Cyberspace, DSC 2021

作者： Liao, Qisheng Wu, Xu Xie, Xiaqing Wu, Jingchen Qiu, Lirong Sun, Lijuan Beijing University Of Posts And Telecommunications Key Laboratory Of Trustworthy Distributed Computing And Service Ministry Of Education Beijing China

ISBN: (纸本)9781665418157

The variational graph autoencoder (VGAE), a framework for unsupervised learning on graph-structured data, has captured more attention recently in graph embedding area. However, it has been faced up with the challenge of KL vanishing, which will converge to local optimum and make the graph embedding unavailable for downstream tasks like link prediction. This paper proposes a novel variational graph autoencoder framework to achieve more effective graph embedding. Firstly, we introduce batch normalization to make sure the KL distribution consistent with the whole dataset by keeping its expectation positive, thus avoiding posteriori collapse. In addition, we invite residual connection and adversarial network to simultaneously embed the topology information and content information into graph representation stably, enhancing the expressive ability of latent vector. Finally, link prediction experiments on three citation datasets demonstrate that the AUC scores of our algorithm are higher than 92% and the average accuracies are higher than 93%, which is competitive comparing with the state-of-the-art variational graph autoencoders. © 2021 IEEE.

关键词： Topology

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Sensitive Image Information Recognition Model of Network Community based on Content Text 6

Sensitive Image Information Recognition Model of Network Com...

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6th IEEE International Conference on Data Science in Cyberspace, DSC 2021

作者： Gao, Li Wu, Xu Wu, Jingchen Xie, Xiaqing Qiu, Lirong Sun, Lijuan Beijing University Of Posts And Telecommunications Key Laboratory Of Trustworthy Distributed Computing And Service Ministry Of Education Beijing China

ISBN: (纸本)9781665418157

With the advent of the multimedia era and the age of picture-reading, the recognition effect of sensitive content of image data has become the key to maintain the information security of network communities. At present, the image classification and recognition technology for sensitive images in network communities cannot obtain the semantic content of images, and it is difficult to combine the image information with the knowledge in network communities, resulting in low recognition accuracy and poor interpretability, and it is difficult to trace the transmission and fermentation of image information in network communities. To solve this problem, this paper proposes a sensitive image information recognition model of network community based on content text by using image caption technology. Through the text description of the image content of the network community, and the integration of a large number of network community text knowledge, the model can finally identify the images containing sensitive content more accurately and more understandable, and the transmission of image information on the network can be traced through the content text. In this paper, MSCOCO(Microsoft Common Objects in Context) dataset and sensitive image self-made dataset of network community are used as the training set. The experimental results show that the method presented in this paper is significantly better than the model based on image classification task in terms of accuracy and traceability of image sensitive information recognition results, which proves the feasibility and effectiveness of sensitive image information recognition in network communities based on content text. © 2021 IEEE.

关键词： Classification (of information)

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Fast algorithms of public key cryptosystem based on Chebyshev polynomials over finite field

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The Journal of China Universities of Posts and Telecommunications 2011年第2期18卷 86-93页

作者： LI Zhi-hui CUI Yi-dong XU Hui-min Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beij ing 100876 China School of Information and Communication engineering Beijing University of Posts and Telecommunications Beijing 100876 China

The computation of Chebyshev polynomial over finite field is a dominating operation for a public key *** generic algorithms with running time of have been presented for this computation：the matrix algorithm and the characteristic polynomial algorithm,which are feasible but not *** this paper,these two algorithms are modified in procedure to get faster execution *** complexity of modified algorithms is still,but the number of required operations is reduced,so the execution speed is ***,a new algorithm relevant with eigenvalues of matrix in representation of Chebyshev polynomials is also presented,which can further reduce the running time of that computation if certain conditions are *** implementations of these algorithms are realized,and the running time comparison is *** an efficient scheme for the computation of Chebyshev polynomial over finite field is presented.

关键词： Chebyshev polynomial algorithm running time square root

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Machine-learning based ocean atmospheric duct forecasting:a hybrid model-data-driven approach

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The Journal of China Universities of Posts and Telecommunications 2023年第4期30卷 1-9页

作者： Feng Yuting Gong Haobing Hao Xiaojing Gao Hui Guo Xiangming Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Radio Wave Propagation Characteristics and Modeling Technology 22nd Research Institute of China Electronics Technology CorporationQingdao 266107China

The atmospheric duct is a vital radio wave *** methods of forecasting the atmospheric duct mainly include statistical analysis based on sounding observation data and mesoscale numerical model-based *** former can provide accurate duct information but is highly dependent on the acquisition of data *** latter is more practical but still lacks *** paper introduces machine learning to establish a novel meteorological parameter correction model for atmospheric duct *** detail,using the weather research and forecasting(WRF)model data and spatiotemporal characteristics as input,sounding data as label and extreme gradient boosting(XGBoost)model for training,the meteorological parameter correction effect is the best,i.e.,the accuracy of forecast meteorological parameters is improved by about 65.4%.Combining the mapping relationship between meteorological parameters and corrected atmospheric refractive index(CARI),and the transition mechanism of CARI to duct parameters,a new duct forecasting mechanism is *** to the high efficiency of numerical model and the accuracy of sounding data,the new duct forecasting mechanism has excellent *** comparing the duct forecasting results,the forecasting accuracy of the new duct forecasting model is significantly higher than that of the mesoscale model.

关键词： marine atmospheric duct neural network fitting machine learning forecasting mechanism

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An efficient confidentiality protection solution for pub/sub system

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Cybersecurity 2023年第4期6卷 14-25页

作者： Jinglei Pei Yuyang Shi Qingling Feng Ruisheng Shi Lina Lan Shui Yu Jinqiao Shi Zhaofeng Ma Beijing University of Posts and Telecommunications Beijing100876China Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of EducationBeijingChina School of Computer Science University of Technology SydneySydneyNSWAustralia

Publish/subscribe(pub/sub)systems are widely used in large-scale messaging systems due to their asynchronous and decoupled *** the population of pub/sub cloud services,the privacy protection problem of pub/sub systems has started to emerge,and events and subscriptions are exposed when executing event matching on untrustworthy cloud ***,as the number of subscriptions increases,the effectiveness of the previous confidentiality protection approaches declines *** this paper,we propose SBM(scalable blind matching),an effective confidentiality protection scheme for pub/sub *** the best of our knowledge,SBM is the first scheme that applies order-preserving encryption algorithm to protect the system’s confidentiality and ensure its *** this scheme,SBM-I is highly effective in subscription matching but is unable to achieve ideal security IND-OCPA,whereas SBM-II is suggested to ensure system security and SGX is used to reduce interaction and boost ciphertext matching *** experiment demonstrates that this method has better matching performance compared to others:the average matching time of SBM-I is 3–4 orders of magnitude faster than the matching algorithm MP and SGX-based algorithm SCBR when the number of subscriptions is 500,000,and the average matching time of SBM-II is 40 times faster than MP and 24 times than SCBR.

关键词： Pub/sub Confidentiality Privacy protection SGX Scalability

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Phishing detection method based on borderline-smote deep belief network 10th

Phishing detection method based on borderline-smote deep bel...

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10th International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, SpaCCS 2017

作者： Zhang, Jiahua Li, Xiaoyong The Key Laboratory of Trustworthy Distributed Computing Service Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9783319723945

With the rapid development of Internet, phishing and other frauds are becoming more and more serious. Criminals posing as banks, electricity providers, social networking sites to send fraudulent information to induce users to log on, steal user information, so that the vast numbers of users and financial institutions suffered property and economic losses. How to accurately and effectively identify phishing related Internet risks has been a major concern of the Internet. This paper analyzes the development history of phishing prevention and control, and presents a Borderline-Smote (Synthetic Minority Over-sampling Technique) DBN (Deeping Belief Network) method to detect phishing. The method uses deep learning phishing detection method based on web documents content and other features to improve 1% on the recognition accuracy. Furthermore the paper uses Borderline-Smote to solve the imbalanced data problem in the training of phishing detection, and further improve 2% on the F-value and recall rate. © Springer International Publishing AG 2017.

关键词： Deep learning

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MTIV: A trustworthiness determination approach for threat intelligence 10th

MTIV: A trustworthiness determination approach for threat in...

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10th International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, SpaCCS 2017

作者： Li, Lei Li, Xiaoyong Gao, Yali Key Laboratory of Trustworthy Distributed Computing Service of the Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9783319723945

With the gradually sharing of threat intelligences, users concern more about their trustworthiness, which is difficult to be judged. Some threat intelligence sharing platforms choose to show the risk or credibility, and inform users the trustworthiness of the threat intelligence. Several researchers have proposed the requirements and techniques for threat intelligence trust assessment. However, they do not present any tool-based or model solutions. In this paper, we present a Trustworthiness Determination Approach for Threat Intelligence (MTIV) to make up these shortcomings. First, we propose a framework to excavate threat intelligence via multiple sharing platforms, and extract multidimensional trustful features of the threat intelligence. Based on these, contributions of dimensional trustful features to the trustworthiness determination can be derived. Then we introduce Deep Belief Network (DBN) to determine the trustworthiness of the threat intelligence. The experimental results verify that MTIV is more effective than traditional methods. Our work will be of benefit to build a more credible threat intelligence sharing platform, and enhance the capability of real-time detection and resisting the cyberspace attacks. © Springer International Publishing AG 2017.

关键词： Network security

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Framework for understanding intention-unbreakable malware

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Science China(Information Sciences) 2023年第4期66卷 174-192页

作者： Tiantian JI Binxing FANG Xiang CUI Zhongru WANG Peng LIAO Shouyou SONG Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of Education Beijing University of Posts and Telecommunications Cyberspace Institute of Advanced Technology Guangzhou University Chinese Academy of Cyberspace Studies Beijing Dig Apis Technology Co. Ltd.

The anti-analysis technology of malware has always been the focus in the cyberspace security field. As malware analysis techniques evolve, malware writers continually employ sophisticated anti-reverse engineering techniques to defeat and evade state-of-the-art analyzers. Therefore, to prepare for unknown attacks, studying malware analysis techniques is insufficient. More importantly, we should study new antianalysis techniques for malware. This paper expands the concept of anti-analysis malware and defines a type of intention-unbreakable malware(IUM). To help defenders fully understand and establish a defense system against the new security threats, this paper systematically discusses IUM from the perspectives of threat discovery, threat modeling, attribute analysis, and threat assessment; in addition, this study also provides the Po C implementations of IUM and demonstrates how attackers can use this type of malware to evade advanced security analysis, that is, accurate identification of target(class) victims and intention concealment in reaching non-target(class) victims. Finally, this paper provides several mitigation directions for combating IUM.

关键词： malware unbreakable property modeling mechanism accurate identification intention concealment

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A Recommendation System Based on Multi-attribute 9

A Recommendation System Based on Multi-attribute

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9th International Conference on service Science, ICSS 2016

作者： Dong, Jiali Li, Xiaoyong Fang, Binxing Beijing University of Post and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (纸本)9781509027279

In recent years, more and more internet companies trend to fuse their products with personal recommendation functions. Although the idea of this business model satisfies characteristic of Internet industry felicitously, scientists and engineers gradually find out that the effect is worse than they expected. The quality of recommendation is limited by cold start, data sparse, lack of learning contextual aware, and so on. One of the most important drawbacks of existing recommendation systems is that they usually use only rating matrix as useful information and not fully consider item attributes. In this paper, we proposed an optimized recommendation system, which based on modeling of items in a multidimensional space of item's attribute. The presented approach takes item attributes, user preferences and user context into consideration, and is mainly composed of two modules: (1) attribute-latent factor model, which is used to show user latent preference for each attribute, (2) multi-attribute model, which is used to show user preference between all attributes. The research is conducted in a practical application environment, and the preliminary experimental results show the validity of the recommendation. © 2016 IEEE.

关键词： Recommender systems

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A Theoretical Framework for Value Co-creation Analysis of Ecological Interaction between 6G and Its Enabling Technologies 24

A Theoretical Framework for Value Co-creation Analysis of Ec...

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Proceedings of the 2024 10th International Conference on Communication and Information Processing

作者： Jiao Zhang Xuyu Zhao Fengshuai Du Xinyue Yan Tianjian Yang Haiyan Yan Jiayin Qi School of Economics and Management Beijing University of Posts and Telecommunications Beijing Beijing China China Mobile Research Institute Beijing Beijing China Scienze Politiche e delle relazioni internazionali University of Palermo Palermo Italy and Huangpu Research School of Guangzhou University Guangzhou University Guangzhou Guangdong China School of Management Fudan University Shanghai Shanghai China School of Management Shanghai University of International Business and Economics Shanghai Shanghai China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou Guangdong China and Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing Beijing China

ISBN: (纸本)9798400717444

As demand for communication increases in terms of rate, reliability and security, 6G networks will provide more extreme communication experiences and richer communication services. It is therefore crucial that 6G enabling technologies are developed in order to meet the performance requirements of 6G networks. This study firstly analyses the core elements and development of the 6G ecosystem, and secondly reviews the ecological interactions of 6G and its enabling technologies. Notably, this study, based on the resource orchestration theory, has explored the three-phase evolution and value co-creation of the ecological interactions of 6G and its enabling technologies and provides a summary of the challenges for future development. The study is expected to provide theoretical support for the future system planning and evolutionary development of 6G ecology and its enabling technology ecology.

关键词： 6G ecology

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