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TPTVer： A Trusted Third Party Based Trusted Verifier for Multi-Layered Outsourced Big Data System in Cloud Environment

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China Communications 2018年第2期15卷 122-137页

作者： Jing Zhan Xudong Fan Lei Cai Yaqi Gao Junxi Zhuang College of Computer Science Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Trusted Computing Beijing China National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing China

Cloud computing is very useful for big data owner who doesn't want to manage IT infrastructure and big data technique details. However, it is hard for big data owner to trust multi-layer outsourced big data system in cloud environment and to verify which outsourced service leads to the problem. Similarly, the cloud service provider cannot simply trust the data computation applications. At last,the verification data itself may also leak the sensitive information from the cloud service provider and data owner. We propose a new three-level definition of the verification, threat model, corresponding trusted policies based on different roles for outsourced big data system in cloud. We also provide two policy enforcement methods for building trusted data computation environment by measuring both the Map Reduce application and its behaviors based on trusted computing and aspect-oriented programming. To prevent sensitive information leakage from verification process,we provide a privacy-preserved verification method. Finally, we implement the TPTVer, a Trusted third Party based Trusted Verifier as a proof of concept system. Our evaluation and analysis show that TPTVer can provide trusted verification for multi-layered outsourced big data system in the cloud with low overhead.

关键词： big data security outsourced ser-vice security MapReduce behavior trustedverification trusted third party

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On the ADEP and DOR Analysis of RIS-aided URLLC Systems with Partial CSI in Smart Factory

On the ADEP and DOR Analysis of RIS-aided URLLC Systems with...

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IEEE Globecom Workshops

作者： Tingnan Bao Haiming Wang Hong-Chuan Yang Wen-Jing Wang Mazen O. Hasna Lenovo Research + Institute Of Computing Technology Chinese Academy of Sciences Beijing China Lenovo Reseach Lenovo Group Limited Bejiing China Department of Electrical and Computer Engineering University of Victoria Victoria Canada Shaanxi Key Laboratory of Information Communication Network and Security XUPT Xi'an China Department of Electrical Engineering Qatar University Doha Qatar

ISBN: (数字)9781665423908

ISBN: (纸本)9781665423915

Factory automation applications impose stringent quality of service (QoS) over ultra-reliable and low-latency communication (URLLC) system. However, the existing wireless systems cannot meet the requirement for these mission-critical industrial internet-of-things (IIoT) applications. In this paper, we consider the reconfigurable intelligent surface (RIS)-aided URLLC systems in the smart factory. To facilitate the performance analysis of the practical situation, the presence of phase error at RIS is discussed. We analyze and derive approximate closed-form expression of average decoding error probability (ADEP) and the closed-form expression of performance limits de-lay outage rate (DOR) under our proposed scenario, respectively. Through analytical results and associated numerical examples, we demonstrate the effect of the number of reflecting elements at the RIS, channel blocklength and packet size in achieving high reliability and low latency. Therefore, the deployment of RIS can offer an attractive solution to enhance the reliability over URLLC systems in the smart factory.

关键词： Wireless communication Closed-form solutions Error probability Rician channels Quality of service Ultra reliable low latency communication Reconfigurable intelligent surfaces

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Train delays prediction based on feature selection and random forest

Train delays prediction based on feature selection and rando...

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International Conference on Intelligent Transportation

作者： Yuanyuan Ji Wei Zheng Hairong Dong Pengfei Gao National Research Center of Railway Safety Assessment Beijing Jiaotong University Beijing China National Research Centre of Railway Safety Assessment and the Beijing Key Laboratory of Intelligent Traffic Data Security and Privacy Protection Technology Beijing Jiaotong University Beijing State Key Lab of Rail Traffic Control & Safety Beijing Jiaotong University Beijing

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

Although trains are more efficient and convenient than other transportation, delays often occur. Accurately predicting the delay time of trains is of great significance to both dispatchers and passengers. The method for predicting the arrival delay time of trains is based on feature selection algorithm and machine learning. First, we collect train delay cases to sort out the delay factors. In addition to internal factors, external factors such as weather and signal failure are also considered. Then, an improved max-relevance and min-redundancy method (mRMR) is used for feature selection. Finally, we apply the method of weighted random forest (wRF) to predict the delay time. The results demonstrate that the feature selection algorithm has a prominent effect on improving the accuracy of the model, and the mean square error based on the weighted random forest has an improvement potential in forecast precision.

关键词： Delays Feature extraction Predictive models Prediction algorithms Rail transportation Correlation Random forests

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Efficient Malware Originated Traffic Classification by Using Generative Adversarial networks

Efficient Malware Originated Traffic Classification by Using...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Zhicheng Liu Shuhao Li Yongzheng Zhang Xiaochun Yun Zhenyu Cheng Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Network Assessment Technology Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

ISBN: (数字)9781728180861

ISBN: (纸本)9781728180878

With the booming of malware-based cyber-security incidents and the sophistication of attacks, previous detections based on malware sample analysis appear powerless due to time-consuming and labor-intensive analysis process. The existing detection methods based on traffic analysis rely heavily on the available traffic patterns, which hinder detecting the zero-day attacks caused by malware variants. In this paper, we propose an approach based on deep learning referred to as TrafficGAN, which analyzes (HTTP) traffic sessions to distinguish between malware-related and normal traffic. We first try to explore traffic patterns of malware variants by adding noise and category condition to the Generative Adversarial networks (GAN), thus generating various similar but slightly different traffic. And then, we use discriminative model to seek the deviation between abnormal traffic and normal traffic by extracting the essential difference. Notablely, we increase the diversity of data by generating samples adversarially, which enhances the robustness of the system to detect zero-day attacks and highlights the lack of sensitive data in the security community. We conduct extensive experiments on the public dataset and our data collected for specific targets. The results demonstrate that our method achieves superior performance to other methods and protects specific targets from the susceptibility of malware.

关键词：

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Generating superpixels for high-resolution images with Decoupled Patch Calibration

arXiv

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arXiv 2021年

作者： Wang, Yaxiong Wei, Yunchao Qian, Xueming Zhu, Li Yang, Yi The School of Software Engineering Xi'an Jiaotong University Xi'an710049 China School of Computer and Information Technology Beijing Jiaotong University Beijing100000 China The Key Laboratory for Intelligent Networks and Network Security Ministry of Education Xi'an Jiaotong University Xi'an710049 China The SMILES Laboratory Xi'an Jiaotong University Xi'an710049 China Zhibian Technology Co. Ltd. Taizhou317000 China The School of Software Xi'an Jiaotong University Xi'an710049 China School of Computer Science and Technology Zhejiang University Hangzhou310000 China

Superpixel segmentation has recently seen important progress benefiting from the advances in differentiable deep learning. However, the very high-resolution superpixel segmentation still remains challenging due to the expensive memory and computation cost, making the current advanced superpixel networks fail to process. In this paper, we devise Patch Calibration networks (PCNet), aiming to efficiently and accurately implement high-resolution superpixel segmentation. PCNet follows the principle of producing high-resolution output from low-resolution input for saving GPU memory and relieving computation cost. To recall the fine details destroyed by the down-sampling operation, we propose a novel Decoupled Patch Calibration (DPC) branch for collaboratively augment the main superpixel generation branch. In particular, DPC takes a local patch from the high-resolution images and dynamically generates a binary mask to impose the network to focus on region boundaries. By sharing the parameters of DPC and main branches, the fine-detailed knowledge learned from high-resolution patches will be transferred to help calibrate the destroyed information. To the best of our knowledge, we make the first attempt to consider the deep-learning-based superpixel generation for high-resolution cases. To facilitate this research, we build evaluation benchmarks from two public datasets and one new constructed one, covering a wide range of diversities from fine-grained human parts to cityscapes. Extensive experiments demonstrate that our PCNet can not only perform favorably against the state-of-the-arts in the quantitative results but also improve the resolution upper bound from 3K to 5K on 1080Ti GPUs. © 2021, CC BY.

关键词： Image segmentation

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False data injection attacks against smart grid state estimation:Construction, detection and defense

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Science China(Technological Sciences) 2019年第12期62卷 2077-2087页

作者： ZHANG Meng SHEN Chao HE Ning HAN SiCong LI Qi WANG Qian GUAN XiaoHong Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi'an Jiao tong UniversityXi'an 710049China Faculty of Electronic and Information Engineering Xi'an Jiaotong UniversityXi'an 710049China School of Mechanical and Electrical Engineering Xi'an University of Architecture and TechnologyXi'an 710049China Institute for Network Sciences and Cyberspace Tsinghua UniversityBeijing 100084China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China

As a typical representative of the so-called cyber-physical system,smart grid reveals its high efficiency,robustness and reliability compared with conventional power ***,due to the deep integration of electrical components and computinginformation in cyber space,smart grid is vulnerable to malicious attacks,especially for a type of attacks named false data injection attacks(FDIAs).FDIAs are capable of tampering meter measurements and affecting the results of state estimation stealthily,which severely threat the security of smart *** to the significantinfluence of FDIAs on smart grid,the research related to FDIAs has received considerable attention over the past *** paper aims to summarize recent advances in FDIAs against smart grid state estimation,especially from the aspects of background materials,construction methods,detection and defense ***,future research directions are discussed and outlined by analyzing existing *** is expected that through the review of FDIAs,the vulnerabilities of smart grid to malicious attacks can be further revealed and more attention can be devoted to the detection and defense of cyber-physical attacks against smart grid.

关键词： false data injection attacks(FDIAs) state estimation smart grid cyber security

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Dynamic NOMA-Based Computation Offloading in Vehicular Platoons

arXiv

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arXiv 2021年

作者： Zheng, Dongsheng Chen, Yingyang Wei, Lai Jiao, Bingli Hanzo, Lajos The Department of Electronics Peking University Beijing100871 China The College of Information Science and Technology Jinan University Guangzhou510632 China The Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Jinan University Guangzhou510632 China The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Both the Mobile edge computing (MEC)-based and fog computing (FC)-aided Internet of Vehicles (IoV) constitute promising paradigms of meeting the demands of low-latency pervasive computing. To this end, we construct a dynamic NOMA-based computation offloading scheme for vehicular platoons on highways, where the vehicles can offload their computing tasks to other platoon members. To cope with the rapidly fluctuating channel quality, we divide the timeline into successive time slots according to the channel's coherence time. Robust computing and offloading decisions are made for each time slot after taking the channel estimation errors into account. Considering a certain time slot, we first analytically characterize both the locally computed source data and the offloaded source data as well as the energy consumption of every vehicle in the platoons. We then formulate the problem of minimizing the long-term maximum task queue by optimizing the allocation of both the communication and computing resources. To solve the problem formulated, we design an online algorithm based on the classic Lyapunov optimization method and successive convex approximation (SCA) method. Finally, the numerical simulation results characterize the performance of our algorithm and demonstrate its advantages both over the local computing scheme and the orthogonal multiple access (OMA)-based offloading scheme. © 2021, CC BY.

关键词： Computation offloading

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Microplastics Pose an Elevated Antimicrobial Resistance Risk Than Natural Surfaces via a Systematic Comparative Study of Surface Biofilms in Rivers

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Environmental science & technology 2025年第22期59卷 2025 May 26页

作者： Kai Yang Fei Xu Xinyu Xing Jing Wei Qing-Lin Chen Jian-Qiang Su Li Cui State Key Laboratory for Ecological Security of Regions and Cities Institute of Urban Environment Chinese Academy of Sciences Xiamen 361021 China. University of Chinese Academy of Sciences 19A Yuquan Road Beijing 100049 China. Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control Nanjing Institute of Environmental Sciences Ministry of Ecology and Environment of China Nanjing 210042 China. Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control CAS Haixi Industrial Technology Innovation Center in Beilun Ningbo 315830 China.

The pervasive presence of antimicrobial resistance (AMR) in various ecosystems threatens global health, especially in persistent biofilm states. Both anthropogenic and natural surfaces in environments provide ideal habitats for biofilm development. Microplastics (MPs), as emerging and rapidly proliferating anthropogenic pollutants, are of particular concern due to their unique physicochemical properties and ubiquity across environments. However, it remains unclear whether biofilms on MPs pose a higher AMR risk compared with natural surfaces. Here, we employed an integrative approach combining phenotypic and genotypic AMR analysis via single-cell spectroscopy and high-throughput quantitative PCR to systematically compare AMR risks on 5 MPs (polyethylene, polystyrene, poly(vinyl chloride), polylactic acid, and Tetra Pak) and 3 natural surfaces (wood, rock, and glass) in an urban aquatic ecosystem. Our results revealed that MPs harbored a higher proportion of metabolically active antibiotic-resistant bacteria and more high-risk antibiotic resistance genes than natural surfaces. By incorporating phenotypic and genotypic AMR with three additional biofilm-related risk factors, including biofilm biomass, microbial activity, and pathogen abundance, the health risk of AMR was quantified and found to be highly dependent on surface types. MPs exhibited, on average, a 10-fold higher health risk than natural surfaces. A structural equation model further identified surface hydrophobicity and microbial diversity as pivotal factors determining AMR risks across different surfaces. This systematic comparison provides new insights into the real-world environmental impact of MPs pollution and underscores the necessity of integrating plastic pollution control into AMR management strategies.

关键词： antimicrobial resistance biofilm health risk assessment microplastics natural surfaces single-cell Raman spectroscopy

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A Dynamic Fault Tree Based CBTC Onboard ATP System Safety Analysis Method*

A Dynamic Fault Tree Based CBTC Onboard ATP System Safety An...

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International Conference on Intelligent Transportation

作者： Pengfei Gao Chao Liu Hairong Dong Wei Zheng National Research Centre of Railway Safety Assessment and the Beijing Key Laboratory of Intelligent Traffic Data Security and Privacy Protection Technology Beijing Jiaotong University Beijing China National Research Centre of Railway Safety Assessment Beijing Jiaotong University Beijing China State Key Lab of Rail Traffic Control & Safety Beijing Jiaotong University Beijing China

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

To obtain refined safety requirements during system architecture design stage, the traditional static fault tree analysis mothed is widely used to analyse the logical relationship between basic hazardous events leading to system hazards in railway signal system, and to identify the weak and key equipment of the system. In order to accurately characterize the dynamic behaviours of the system and improve the accuracy and credibility of analysis results, a dynamic fault tree analysis mothed of railway signal system based on failure propagation modelling was proposed. According to failure logic modelling of dynamic failure behaviours, conforming to signal system function design, component failure model was established within SimFIA platform, and fault trees of system hazards were attained by model simulation. The result of Communication-Based Train Control (CBTC) System on-board equipment case study shows that the fault tree of complex system function are generated by the dynamic fault tree analysis mothed based on Failure Propagation and Transformation Notation (FPTN) modelling, which guaranteed the safety analysis outcomes was accurate and credible.

关键词： Safety Analytical models System analysis and design Fault trees Rail transportation Discrete Fourier transforms Systems architecture

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Factors correlation mining on railway accidents using association rule learning algorithm

Factors correlation mining on railway accidents using associ...

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International Conference on Intelligent Transportation

作者： Yakun Wang Wei Zheng Hairong Dong Pengfei Gao National Research Center of Railway Safety Assessment Beijing Jiaotong University Beijing China National Research Centre of Railway Safety Assessment and the Beijing Key Laboratory of Intelligent Traffic Data Security and Privacy Protection Technology Beijing Jiaotong University Beijing China State Key Lab of Rail Traffic Control & Safety Beijing Jiaotong University Beijing China

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

Although much research work for the operation safety has been taken in the railway domain, some accidents still occur because past experiences of accident analysis were not fully accumulated for safety improvement. This study aims to identify potential causal relationships among the many factors playing a role in railway accidents. A new interestingness measure, Confidence_interestingness ("C_" Inter) and corresponding improved algorithm, Positive and Negative Association Rules Algorithm based on "C_" Inter (PNARA_CI) were put forward in our study. Compared with traditional association rule mining algorithms, the PNARA_CI does not generate candidate association rules by means of frequent itemsets, but by the combination between every two accident factors, which can mine the positive and negative association rules with practical value to the maximum. And they were applied to railway accidents data to explore the association rules of the causal factors in the case study. The effectiveness of the algorithm was verified.

关键词： Railway accidents Data mining Rail transportation Itemsets Safety Databases Task analysis

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