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Correlation leakage analysis based on masking schemes

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science China(Information sciences) 2022年第2期65卷 243-245页

作者： Jiawei ZHANG Yongchuan NIU An WANG Data Communication science and Technology Research Institute school of computer science Beijing Institute of Technology

Dear editor,Masking is generally utilized to construct the first-order protection for cryptographic algorithms,but such protected designs are still susceptible to higher-order power analysis ***-order differential power analysis (DPA)[1–4]can break first-order masking countermeasures by combining the leakages of the two secret shares into a signal that is correlated with the target intermediate variable.

关键词： Correlation leakage analysis Correlation leakage model Second-order analysis Side-channel attack Masking schemes

Experience Availability： Tail-Latency Oriented Availability in Software-Defined Cloud Computing

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Journal of computer science & Technology 2017年第2期32卷 250-257页

作者： Bin-Lei Cai Rong-Qi Zhang Xiao-Bo Zhou Lai-Ping Zhao Ke-Qiu Li Tianjin Key Laboratory of Advanced Networking School of Computer Science and Technology Tianjin University Tianjin 300350 China school of computer Software Tianjin University Tianjin 300350 China

Resource sharing, multi-tenant interference and bursty workloads in cloud computing lead to high tail-latency that severely affects user quality of experience （QoE）, where response latency is a critical factor. A lot of research efforts are dedicated to reducing high tail-latency and improving user QoE, such as software-defined cloud computing （SDC）. However, the traditional availability analysis of cloud computing captures the pure failure-repair behavior with user QoE ignored. In this paper, we propose a conceptual framework, experience availability, to properly assess the effectiveness of SDC while taking into account both availability and response latency simultaneously. We review the related work on availability models and methods of cloud systems, and discuss open problems for evaluating experience availability in SDC. We also show some of our preliminary results to demonstrate the feasibility of our ideas.

关键词： cloud computing software-defined cloud computing (SDC) availability tail-latency

Within-Project and Cross-Project Software Defect Prediction Based on Improved Transfer Naive Bayes Algorithm

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computers, Materials & Continua 2020年第5期63卷 891-910页

作者： Kun Zhu Nana Zhang Shi Ying Xu Wang school of computer science Wuhan UniversityWuhan430072China Department of computer science Vrije University AmsterdamAmsterdam1081HVThe Netherlands

With the continuous expansion of software scale,software update and maintenance have become more and more ***,frequent software code updates will make the software more likely to introduce new *** how to predict the defects quickly and accurately on the software change has become an important problem for software *** defect prediction methods often cannot reflect the feature information of the defect comprehensively,and the detection effect is not ideal ***,we propose a novel defect prediction model named ITNB(Improved Transfer Naive Bayes)based on improved transfer Naive Bayesian algorithm in this paper,which mainly considers the following two aspects:(1)Considering that the edge data of the test set may affect the similarity calculation and final prediction result,we remove the edge data of the test set when calculating the data similarity between the training set and the test set;(2)Considering that each feature dimension has different effects on defect prediction,we construct the calculation formula of training data weight based on feature dimension weight and data gravity,and then calculate the prior probability and the conditional probability of training data from the weight information,so as to construct the weighted bayesian classifier for software defect *** evaluate the performance of the ITNB model,we use six datasets from large open source projects,namely Bugzilla,Columba,Mozilla,JDT,Platform and *** compare the ITNB model with the transfer Naive Bayesian(TNB)*** experimental results show that our ITNB model can achieve better results than the TNB model in terms of accurary,precision and pd for within-project and cross-project defect prediction.

关键词： Cross-project defect prediction transfer Naive Bayesian algorithm edge data similarity calculation feature dimension weight

Energy-Theft Detection Issues for Advanced Metering Infrastructure in Smart Grid

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Tsinghua science and Technology 2014年第2期19卷 105-120页

作者： Rong Jiang Rongxing Lu Ye Wang Jun Luo Changxiang Shen Xuemin(Sherman) Shen school of computer National University of Defense Technology Department of electrical and computer Engineering University of Waterloo school of electrical and Electronics Engineering Nanyang Technological University Communication Engineering Research Center Harbin Institute of Technology Shenzhen Graduate School Computing Technology Institute of China Navy

With the proliferation of smart grid research, the Advanced Metering Infrastructure （AMI） has become the first ubiquitous and fixed computing platform. However, due to the unique characteristics of AMI, such as complex network structure, resource-constrained smart meter, and privacy-sensitive data, it is an especially challenging issue to make AMI secure. Energy theft is one of the most important concerns related to the smart grid implementation. It is estimated that utility companies lose more than S25 billion every year due to energy theft around the world. To address this challenge, in this paper, we discuss the background of AMI and identify major security requirements that AMI should meet. Specifically, an attack tree based threat model is first presented to illustrate the energy-theft behaviors in AMI. Then, we summarize the current AMI energy-theft detection schemes into three categories, i.e., classification-based, state estimation-based, and game theory-based ones, and make extensive comparisons and discussions on them. In order to provide a deep understanding of security vulnerabilities and solutions in AMI and shed light on future research directions, we also explore some open challenges and potential solutions for energy-theft detection.

关键词： smart grid Advanced Metering Infrastructure (AMI) security energy-theft detection

Community-Based User Domain Model Collaborative Recommendation Algorithm

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Tsinghua science and Technology 2013年第4期18卷 353-359页

作者： Fulan Qian Yanping Zhang Yuan Zhang Zhen Duan the school of computer science and Technology Anhui University the school of Electronic and Information Engineering Anhui Jianzhu University

Collaborative Filtering （CF） is a commonly used technique in recommendation systems. It can promote items of interest to a target user from a large selection of available items. It is divided into two broad classes： memory-based algorithms and model-based algorithms. The latter requires some time to build a model but recommends online items quickly, while the former is time-consuming but does not require pre-building time. Considering the shortcomings of the two types of algorithms, we propose a novel Community-based User domain Collaborative Recommendation Algorithm （CUCRA）. The idea comes from the fact that recommendations are usually made by users with similar preferences. The first step is to build a user-user social network based on users＇ preference data. The second step is to find communities with similar user preferences using a community detective algorithm. Finally, items are recommended to users by applying collaborative filtering on communities. Because we recommend items to users in communities instead of to an entire social network, the method has perfect online performance. Applying this method to a collaborative tagging system, experimental results show that the recommendation accuracy of CUCRA is relatively good, and the online time-complexity reduces to O.（n）.

关键词： collaborative recommendation tagging system community-based recommendation system

Reveal training performance mystery between Tensor Flow and PyTorch in the single GPU environment

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science China(Information sciences) 2022年第1期65卷 147-163页

作者： Hulin DAI Xuan PENG Xuanhua SHI Ligang HE Qian XIONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabSchool of Computer Science and Technology Huazhong University of Science and Technology Department of computer science University of Warwick

Deep learning has gained tremendous success in various fields while training deep neural networks(DNNs) is very compute-intensive, which results in numerous deep learning frameworks that aim to offer better usability and higher performance to deep learning practitioners. Tensor Flow and Py Torch are the two most popular frameworks. Tensor Flow is more promising within the industry context, while Py Torch is more appealing in academia. However, these two frameworks differ much owing to the opposite design philosophy:static vs dynamic computation graph. Tensor Flow is regarded as being more performance-friendly as it has more opportunities to perform optimizations with the full view of the computation graph. However, there are also claims that Py Torch is faster than Tensor Flow sometimes, which confuses the end-users on the choice between them. In this paper, we carry out the analytical and experimental analysis to unravel the mystery of comparison in training speed on single-GPU between Tensor Flow and Py Torch. To ensure that our investigation is as comprehensive as possible, we carefully select seven popular neural networks, which cover computer vision, speech recognition, and natural language processing(NLP). The contributions of this work are two-fold. First, we conduct the detailed benchmarking experiments on Tensor Flow and Py Torch and analyze the reasons for their performance difference. This work provides the guidance for the end-users to choose between these two frameworks. Second, we identify some key factors that affect the performance,which can direct the end-users to write their models more efficiently.

关键词： deep learning performance comparison TensorFlow PyTorch

A Bandwidth-Link Resources Cooperative Allocation Strategy of Data Communication in Intelligent Transportation Systems

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China Communications 2019年第4期16卷 234-249页

作者： Xiaoming Jiang Kangfei Li Haobin Jiang Na Zhu Xin Tong school of computer science and Communication Engineering Jiangsu University school of Automotive and Transportation Engineering Jiangsu University

The bandwidth resources allocation strategies of the existing Internet of Vehicles(IoV) are mainly base on the communication architecture of the traditional 802.11 x in the wireless local area network(WLAN). The traditional communication architecture of IoV will easily cause significant delay and low Packet Delivery Ratio(PDR) for disseminating critical security beacons under the condition of high-speed movement, distance-varying communication, and mixed traffic. This paper proposes a novel bandwidth-link resources cooperative allocation strategy to achieve better communication performance under the road conditions of intelligent transportation systems(ITS). Firstly, in traffic scenarios, based on the characteristic to predict the relative position of the mobile transceivers, a strategy is developed to cooperate on the mobile cellular network and the Dedicated Short-Range Communications(DSRC). Secondly, by adopting the general network simulator NS3, the dedicated mobile channel models that are suitable for the data interaction of ITS, is applied to confirm the feasibility and reliability of the strategy. Finally, by the simulation, comparison, and analysis of some critical performance parame-ters, we conclude that the novel strategy does not only reduce the system delay but also improve the other communication performance indicators, such as the PDR and communication capacity.

关键词： IoV bandwidth-link resources cooperative allocation strategy system delay PDR

Optimally Connected Hybrid Complex Networks with Windmill Graphs Backbone

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Journal of Systems science & Complexity 2020年第4期33卷 903-929页

作者： SAFAEI Farshad BABAEI Amin MOUDI Mehrnaz Faculty of computer science and Engineering Shahid Beheshti UniversityTehranIran school of computer science Institute for Research in Fundamental SciencesTehranIran Department of computer Engineering University of Torbat HeydariehTorbat HeydariehIran

The significance of the existing analysis methods in complex networks and easy access to the ever-increasing volume of information present the emergence of proposing new methods in various fields based on complex system ideas. However, these systems are usually faced with various random failures and intelligent attacks. Due to the nature of the components’ behaviors, the occurrence of the failures and faults in their operations and the alteration of their topologies are the most important problems. Since the complex systems are usually used as the infrastructures of other networks, their robustness against failures and the adoption of suitable precautions are necessary. Moreover, the smallworld effect in most complex systems is one of the crucial structural features. The authors found that the relation between these two is not well-known and may even be in conflict in some networks. The main goal in this paper is to achieve an optimal topology by utilizing a robustness-oriented multiobjective trade-off optimization model(edge rewiring) to establish a peaceful relationship between the two requirements. By offering a proposed rewiring method with the small-world effect, which is called core-periphery Windmill property, the authors demonstrated that the generated networks are able to exhibit appropriate robustness even during intelligent attacks. The results obtained in terms of Windmill graphs are presented very good approximations to demonstrate the small-world effect. These graphs are used as the initial core in the construction of the optimized networks’ topologies.

关键词： effect. utilizing Optimal

Dynamic Multi-Physics Behaviors and Performance Loss of Cylindrical Batteries Upon Low-Velocity Impact Loading

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Energy & Environmental Materials 2024年第6期7卷 184-192页

作者： Qingdan Huang Yang Bai Han Luo Yikai Jia Chao Zhang school of Civil Aviation Northwestern Polytechnical UniversityXi’an 710072China Key Laboratory on the Impact Protection and Safety Assessment of Civil Aviation Vehicle Taicang 215400China school of Aeronautics Northwestern Polytechnical UniversityXi’an 710072China EVE Energy Co. Ltd.Huizhou 516006C

In challenging operational environments,Lithium-ion batteries(LIBs)inevitably experience mechanical stresses,including impacts and extrusion,which can lead to battery damage,failure,and even the occurrence of fire and explosion ***,it is imperative to investigate the safety performance of LIBs under mechanical *** study is grounded in a more realistic coupling scenario consisting of electrochemical cycling and low-velocity *** systematically and experimentally uncovered the mechanical,electrochemical,and thermal responses,damage behavior,and corresponding mechanisms under various *** study demonstrates that higher impact energy results in increased structural stiffness,maximum temperature,and maximum voltage ***,heightened impact energy significantly influences the electrical resistance parameters within the internal *** also examined the effects of State of Charge(SOC)and *** methodology and experimental findings will offer insights for enhancing the safety design,conducting risk assessments,and enabling the cascading utilization of energy storage systems based on LIBs.

关键词： cycling performance damaged battery lithium-ion battery low-velocity impact mechanical safety