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23rd IEEE International Conference on Automation and computing, ICAC 2017

作者： Yang, Jimeng Chen, Yi Huang, Weidong Li, Yun School of Engineering University of Glasgow GlasgowG12 8LT United Kingdom School of Computer Science and Network Security Dongguan University of Technology Dongguan523808 China State Key Laboratory of Solidification Processing Northwestern Polytechnical University Xi'an710072 China

ISBN: (纸本)9780701702618

Additive manufacturing of three-dimensional objects are now more and more realised through 3D printing, known as an evolutional paradigm in the manufacturing industry. Artificial intelligence is currently finding wide applications to 3D printing for an intelligent, efficient, high quality, mass customised and service-oriented production process. This paper presents a comprehensive survey of artificial intelligence in 3D printing. Before a printing task begins, the printability of given 3D objects can be determined through a printability checker using machine learning. The prefabrication of slicing is accelerated through parallel slicing algorithms and the path planning is optimised intelligently. In the aspect of service and security, intelligent demand matching and resource allocation algorithms enable a Cloud service platform and evaluation model to provide clients with an on-demand service and access to a collection of shared resources. We also present three machine learning algorithms to detect product defects in the presence of cyber-attacks. Based on the reviews on various applications, printability with multi-indicators, reduction of complexity threshold, acceleration of prefabrication, real-time control, enhancement of security and defect detection for customised designs are seen of good opportunities for further research, especially in the era of Industry 4.0. © 2017 Chinese Automation and computing Society in the UK - CACSUK.

关键词： Machine learning

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A simple brain storm optimization algorithm via visualizing confidence intervals 11th

A simple brain storm optimization algorithm via visualizing ...

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11th International Conference on Simulated Evolution and Learning, SEAL 2017

作者： Cao, YingYing Chen, Wei Cheng, Shi Sun, Yifei Liu, Qunfeng Li, Yun Shi, Yuhui School of Computer Science and Network Security Dongguan University of Technology Dongguan China School of Computer Science Shaanxi Normal University Xi’an China Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an China School of Physics and Information Technology Shaanxi Normal University Xi’an China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9783319687582

Visualizing confidence intervals method developed recently for benchmarking stochastic optimization algorithm is adopted in this paper to benchmark and study the brain storm optimization algorithm in depth. Through analyzing numerical effects of different components of brain storm optimization, a simplified brain storm optimization algorithm is developed. It is tested and shown to perform better than the original brain storm optimization algorithm in the objective space. © Springer International Publishing AG 2017.

关键词： Optimization

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An Improved Algorithm for Multivariate Polynomial Interpolation

An Improved Algorithm for Multivariate Polynomial Interpolat...

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第二届亚太计算机科学与应用学术会议

作者： Min Tang Guoqiang Deng School of Mathematics and Computing Science Guangxi Key Laboratory of Cryptography and Information SecurityGuilin University of Electronic Technology Shanghai Key Lab of Trustworthy Computing East China Normal University School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and ComputationGuilin University of Electronic Technology

In this paper, we present an improved method based on Zippel's algorithm for multivariate polynomial interpolation, and a testing point technique is proposed to verify the recovered polynomial. Compared with Zippel's algorithm, the new method not only returns an exact target polynomial but takes less computation time as well. The experiments show the effectiveness of the improved method.

关键词： multivariate polynomial interpolation Zippel's algorithm testing point

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An Improved Algorithm for Multivariate Polynomial Interpolation

An Improved Algorithm for Multivariate Polynomial Interpolat...

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2017 Asia-Pacific Computer Science and Application Conference

In this paper,we present an improved method based on Zippel's algorithm for multivariate polynomial interpolation,and a testing point technique is proposed to verify the recovered *** with Zippel's algorithm,the new method not only returns an exact target polynomial but takes less computation time as *** experiments show the effectiveness of the improved method.

关键词： multivariate polynomial interpolation Zippel's algorithm testing point

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A fibonacci based batch auditing protocol for cloud data 1

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9th International Symposium on Cyberspace Safety and security, CSS 2017

作者： Shen, Jian Shen, Jun Wang, Chen Wang, Anxi Jiangsu Engineering Center of Network Monitoring Nanjing China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (数字)9783319694719

ISBN: (纸本)9783319694702

As cloud storage is developing fast with time going by, the design of auditing protocols has caught a large number of researchers’ eyes. However, though most of the existing auditing protocols have considered batch auditing to save resources in the auditor side, the design of methods to locate the specific positions of the corrupted data blocks is ignored. In this paper, we propose a novel batch auditing protocol based on the Fibonacci sequence to save resources in the cloud, which is an extension of our previous work. Experimental results and numerical analysis indicate that the proposed scheme is efficient. © 2017, Springer International Publishing AG.

关键词： Cloud storage

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Discovering and understanding the security hazards in the interactions between iot devices, mobile apps, and clouds on smart home platforms

arXiv

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arXiv 2018年

作者： Zhou, Wei Jia, Yan Yao, Yao Zhu, Lipeng Guan, Le Mao, Yuhang Liu, Peng Zhang, Yuqing National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences China School of Cyber Engineering Xidian University China Department of Computer Science University of Georgia United States College of Information Sciences and Technology Pennsylvania State University United States State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences China

A smart home connects tens of home devices to the Internet, where an IoT cloud runs various home automation applications. While bringing unprecedented convenience and accessibility, it also introduces various security hazards to users. Prior research studied smart home security from several aspects. However, we found that the complexity of the interactions among the participating entities (i.e., devices, IoT clouds, and mobile apps) has not yet been systematically investigated. In this work, we conducted an in-depth analysis of five widely-used smart home platforms. Combining firmware analysis, network traffic interception, and black-box testing, we reverse-engineered the details of the interactions among the participating entities. Based on the details, we inferred three legitimate state transition diagrams for the three entities, respectively. Using these state machines as a reference model, we identified a set of unexpected state transitions. To confirm and trigger the unexpected state transitions, we implemented a set of phantom devices to mimic a real device. By instructing the phantom devices to intervene in the normal entity-entity interactions, we have discovered several new vulnerabilities and a spectrum of attacks against real-world smart home platforms. Copyright © 2018, The Authors. All rights reserved.

关键词： Automation

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Packet： a privacy-aware access control policy composition method for services composition in cloud environments

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Frontiers of Computer Science 2016年第6期10卷 1142-1157页

作者： Li LIN Jian HU Jianbiao ZHANG College of Computer Science Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Trusted Computing Beijing 100124 China National Engineering Laboratory for Classified Information Security Protection Beijing 100124 China

Combining different independent cloud services must coordinate their access control policies. Otherwise unauthorized access to composite cloud service can occur when there＇s a conflict among different cloud service providers＇ access control policies, and then it will bring serious data security and privacy issues. In this paper, we propose Packet, a novel access control policy composition method that can detect and resolve policy conflicts in cloud service composition, including those conflicts related to privacyaware purposes and conditions. The Packet method is divided into four steps. First, employing a unified description, heterogeneous policies are transformed into a unified attributebased format. Second, to improve the conflict detection ef- ficiency, policy conflicts on the same resource can be eliminated by adopting cosine similarity-based algorithm. Third, exploiting a hierarchical structure approach, policy conflicts related to different resources or privacy-aware purposes and conditions can be detected. Fourth, different conflict resolution techniques are presented based on the corresponding conflict types. We have successfully implemented the Packet method in Openstack platform. Comprehensive experiments have been conducted, which demonstrate the effectiveness of the proposed method by the comparison with the existing XACML-based system at conflict detection and resolution performance.

关键词： cloud service composition access control privacy policy composition unified policy format conflict detection similarity analysis conflict resolution

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EagleMine: Vision-guided mining in large graphs

arXiv

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arXiv 2017年

作者： Feng, Wenjie Liu, Shenghua Faloutsos, Christos Hooi, Bryan Shen, Huawei Cheng, Xueqi Cas Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences School of Computer Science Carnegie Mellon University

Given a graph with millions of nodes, what patterns exist in the distributions of node characteristics, and how can we detect them and separate anomalous nodes in a way similar to human vision? In this paper, we propose a vision-guided algorithm, EagleMine, to summarize micro-cluster patterns in two-dimensional histogram plots constructed from node features in a large graph. EagleMine utilizes a water-level tree to capture cluster structures according to vision-based intuition at multi-resolutions. EagleMine traverses the waterlevel tree from the root and adopts statistical hypothesis tests to determine the optimal clusters that should be fitted along the path, and summarizes each cluster with a truncated Gaussian distribution. Experiments on real data show that our method can find truncated and overlapped elliptical clusters, even when some baseline methods split one visual cluster into pieces with Gaussian spheres. To identify potentially anomalous micro-clusters, EagleMine also a designates score to measure the suspiciousness of outlier groups (i.e. node clusters) and outlier nodes, detecting bots and anomalous users with high accuracy in the real Microblog data. Copyright © 2017, The Authors. All rights reserved.

关键词： Statistics

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MBSA: a lightweight and flexible storage architecture for virtual machines

MBSA: a lightweight and flexible storage architecture for vi...

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作者： Chen, Xian Chen, Wenzhi Lu, Zhongyong Zhang, Yu Chang, Rui Hassan, Mohammad Mehedi Alelaiwi, Abdulhameed Xiang, Yang College of Computer Science and Technology Zhejiang University Hangzhou China State Key Laboratory of Mathematic Engineering and Advanced Computing Zhengzhou China College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Centre for Cyber Security Research Deakin University BurwoodVIC Australia

With the advantages of extremely high access speed, low energy consumption, nonvolatility, and byte addressability, nonvolatile memory (NVM) device has already been setting off a revolution in storage field. Conventional storage architecture needs to be optimized or even redesigned from scratch to fully explore the performance potential of NVM device. However, most previous NVM-related works only explore its low access latency and low energy consumption. Few works have been done to explore the appropriate way to use NVM device for improving virtual machine's storage performance. In this paper, we comprehensively evaluate and analyze conventional virtual machine's storage architecture. We find that, even with cutting-edge optimization technologies, virtual machine can only achieve 30% of NVM device's original performance. Based on this observation, we propose a memory bus–based storage architecture, which we named MBSA. Memory bus–based storage architecture can greatly shorten the length of virtual machine's storage input/output stack and improve NVM device's use flexibility. In addition, an efficient wear-leveling algorithm is proposed to prolong NVM device's lifespan. To evaluate the new architecture, we implement it as well as the wear-leveling algorithm on real hardware and software platform. Experimental results show that MBSA can provide a big performance improvement, about 2.55X, and the wear-leveling algorithm can efficiently balance write operations on NVM device with a negligible performance overhead (no more than 3%). Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Virtual machine

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Analysis of SVEIR worm attack model with saturated incidence and partial immunization

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Journal of Communications and Information networks 2017年第4期1卷 105-115页

作者： Wang, Fangwei Huang, Wenyan Shen, Yulong Wang, Changguang College of Information Technology Hebei Normal University Shijiazhuang China Shannxi Key Laboratory of Network and System Security Xidian University Xi’an China College of Computers Xidian University Xi’an China

Internet worms can propagate across networks at terrifying speeds, reduce network security to a remarkable extent, and cause heavy economic losses. Thus, the rapid elimination of Internet worms using partial immunization becomes a significant matter for sustaining Internet infrastructure. This paper addresses this issue by presenting a novel worm susceptible-vaccinated-exposed-infectious-recovered model, named the SVEIR model. The SVEIR model extends the classical susceptible-exposed-infectious-recovered model (refer to SEIR model) through incorporating a saturated incidence rate and a partial immunization rate. The basic reproduction number in the SVEIR model is obtained. By virtue of the basic reproduction number, we prove the global stabilities of an infection-free equilibrium point and a unique endemic equilibrium point. Numerical methods are used to verify the proposed SVEIR model. Simulation results show that partial immunization is highly effective for eliminating worms, and the SVEIR model is viable for controlling and forecasting Internet worms.

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