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物联网与移动计算青年学术论坛

来源: 点击: 时间: 2017年06月15日 13:57

报告时间:6月17日上午8:50-11:20,下午2:00-5:00

报告地点:中南大学校本部计算机楼212会议室

报告人:张宽,加拿大滑铁卢大学博士后,Assistant Professor, Department of Electrical and Computer Engineering, University of Nebraska, Lincoln, US

报告题目:Big Data Analysis and Security for Mobile Social Networks

报告内容:Mobile Social Network (MSN), as an emerging social network platform, has become increasingly popular and brought immense benefits. However, big data challenges and security concerns rise as the boom of MSN applications comes up. In this talk, we will present big data and security challenges in MSNs, and introduce big data analysis solutions. First, to detect misbehaviors during data sharing, we present a social-based mobile Sybil detection scheme (SMSD). The SMSD analyzes user's social behaviors during networking and detects Sybil attackers by differentiating the abnormal pseudonym changing and contact behaviors, since Sybil attackers usually frequently or rapidly change their pseudonyms to cheat legitimate users. Then, we introduce a social network based infection analysis system, to analyze the instantaneous infectivity during human-to-human contact. We also present privacy-preserving data query and classification methods to achieve big data analysis and privacy in this infection analysis system. This talk will close with a brief discussion of future work on big data and security.

个人简介:Dr. Kuan Zhang received his Ph.D. degree in Electrical and Computer Engineering from the University of Waterloo, Canada, in 2016. He received B.Sc. degree in Communication Engineering and M.Sc. degree in Computer Science from Northeastern University, China, in 2009 and 2011, respectively. Currently, he is a postdoctoral fellow with the Department of Electrical and Computer Engineering, University of Waterloo. His research interests include big data analysis and security for mobile social networks, mobile healthcare, cyber physical system, and cloud computing.

报告人:邓瑞龙,加拿大阿尔伯塔大学博士后

报告题目:Coordinated Cyber-Physical Attacks and Countermeasures in Smart Grid

报告内容:Smart grid, as one of the most critical infrastructures, is vulnerable to a wide variety of cyber and/or physical attacks. Recently, a new category of threats to smart grid, named coordinated cyber-physical attacks (CCPAs), are emerging. A key feature of CCPAs is to leverage cyber attacks to mask physical attacks which can cause power outages and potentially trigger cascading failures.

In this talk, we investigate CCPAs in smart grid and show that an adversary can carefully synthesize a false data injection attack vector based on phasor measurement unit (PMU) measurements to neutralize the impact of physical attack vector, such that CCPAs could circumvent bad data detection without being detected. Specifically, we present two potential CCPAs, namely replay and optimized CCPAs, respectively, and analyze the adversary's required capability to construct them. Based on the analytical results, countermeasures are proposed to detect the two kinds of CCPAs, through known-secure PMU measurement verification (in the cyber space) and online tracking of the power system equivalent impedance (in the physical space), respectively. The implementation of CCPAs in smart grid and the effectiveness of countermeasures are demonstrated by using an illustrative 4-bus power system and the IEEE 9-bus, 14-bus, 30-bus, 118-bus, and 300-bus test power systems.

个人简介:Ruilong Deng received the B.Sc. and Ph.D. degrees both in Control Science and Engineering from Zhejiang University, Hangzhou, Zhejiang, China, in 2009 and 2014, respectively.

He was a Visiting Scholar at Simula Research Laboratory, Fornebu, Norway, in 2011, and the University of Waterloo, Waterloo, ON, Canada, from 2012 to 2013. He was a Research Fellow at Nanyang Technological University, Singapore, from 2014 to 2015. Currently, he is an AITF Postdoctoral Fellow with the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada. His research interests include smart grid, cyber security, and wireless sensor network.

Dr. Deng serves/served as an Editor for IEEE/KICS Journal of Communications and Networks, and a Guest Editor for IEEE Transactions on Emerging Topics in Computing and IET Cyber-Physical Systems: Theory & Applications. He also serves/served as a Technical Program Committee (TPC) Member for IEEE GLOBECOM, IEEE ICC, IEEE SmartGridComm, EAI SGSC, etc.

报告人:何建平,上海交通大学副研究员

报告题目:Analysis of Privacy-preserving Average Consensus: Condition and Optimal Distribution

报告内容:The goal of the privacy-preserving average consensus (PPAC) is to guarantee the privacy of initial state and asymptotic consensus on the exact average of the initial value. This goal is achieved by an existing PPAC algorithm by adding and subtracting variance decaying and zero-sum random noises to the consensus process. However, there is lack of theoretical analysis to quantify the degree of the privacy protection. In this work, we analyze the privacy of the PPAC algorithm in the sense of the maximum disclosure probability that the other nodes can infer one node’s initial state within a given small interval. We first propose a new privacy definition, named (ϵ,σ)-privacy, to depict the maximum disclosure probability. Then, we prove that PPAC is an (ϵ,σ)-privacy algorithm, and obtain the closed-form expression of the relationship between ϵ and σ. We also prove that the added noises with uniform distribution is optimal for the PPAC algorithm to achieve the highest (ϵ,σ)-privacy. Finally, we prove that the disclosure probability will converge to one when all information used in consensus process is available, i.e., the privacy is compromised. Simulations are conducted to verify the results.

个人简介:Jianping He iscurrentlyan associate research fellow in the Department of Automation at Shanghai Jiao Tong University. He received the Ph.D. degree in control science and engineering from Zhejiang University, Hangzhou, China, in 2013, andhad been a research fellow in the Department of Electrical and Computer Engineering at University of Victoria, Canada, from Dec. 2013 to Mar. 2017. His research interests include the control and optimization of cyber-physical systems, the scheduling and optimization in VANETs, security and privacy in distributed networks and the investment decision in financial market and electricity market. Dr. He serves as an Associate Editor for the KSII Transactions on Internet and Information Systems. He was also a Guest Editor of the International Journal of Robust and Nonlinear Control, Neurocomputing, and the International Journal of Distributed Senor Networks. He was the winner of Outstanding Thesis Award, Chinese Association of Automation, 2015.

报告人:吴远,浙江工业大学副教授,浙江省杰出青年基金获得者

报告题目:Secrecy-Based Energy-Efficient Data Offloading via Dual Connectivity Over Unlicensed Spectrums

报告内容:Offloading cellular mobile users’ (MUs’) data traffic to small-cell networks is a cost-effective approach to relieve congestion in macrocell cellular networks. However, as many small-cell networks operate in the unlicensed bands, the data offloading might suffer from a security issue, i.e., some eavesdropper could overhear the offloaded data over unlicensed spectrums. This motivates us to investigate a secrecy-based energy-efficient uplink data offloading scheme. Specifically, we consider the recent paradigm of traffic offloading via dual connectivity, which enables an MU to simultaneously deliver traffic to a macro base station (mBS) over the licensed channel and a small-cell access point (sAP) over the unlicensed channel. We formulate an MU’s joint optimization of traffic scheduling and power allocation problem, with the objective of minimizing the total power consumption while meeting both the MU’s traffic demand and secrecy requirement. Despite the non-convex nature of the joint optimization problem, we propose an efficient algorithm to compute the optimal offloading solution. By evaluating the impact of the MU’s secrecy requirement and the eavesdropper’s channel condition, we quantify the conditions under which the optimal offloading solution corresponds to the full-offloading and zero-offloading, respectively. Numerical results validate the optimal performance of our proposed algorithm, and show that the optimal offloading can significantly reduce the total power consumption compared with some fixed offloading schemes. Based on the optimal offloading solution for each MU, we further analyze the scenario of multiple MUs and sAPs, and investigate how to optimally exploit the sAPs’ total offloading capacity to serve the MUs while accounting for the MUs’ corresponding power consumptions for offloading data. To this end, we formulate a total network-benefit maximization problem that accounts for the reward for serving the MUs successfully, the mBS’s bandwidth usage, and the MUs’ power consumptions.

个人简介:吴远,博士,IEEE高级会员,浙江工业大学信息工程学院副教授,浙江省自然科学基金杰出青年基金获得者。吴远2010年获香港科技大学电子与计算机工程学系博士学位、2010年至2011年任港科大电子与计算机学系博士后研究员。吴远博士分别在美国普林斯顿大学、美国乔治亚州立大学、澳门科技大学担任访问学者;2016-2017年获得国家留学基金访问学者项目资助在加拿大滑铁卢大学进行访学研究。吴远博士长期从事无线网络资源优化管理、认知网络、智能电网研究领域内研究;发表SCI索引期刊论文40余篇,Google总引用750余次。吴远博士获得IEEE通信分会年度旗舰会议IEEE International Conference on Communications (ICC’2017)大会最佳论文奖。

报告人:周毅,河南大学副教授

报告题目:智慧港口时空信息云平台研发及产业化

报告内容:智慧港口是指充分利用物联网、云计算、大数据决策分析与优化等先进技术,针对港口供应链各环节的关键信息进行透彻感知、广泛连接和深度计算,实现港口供应链上各种资源和各个参与方之间的无缝链接与协调联动,从而对港口管理运作做出智慧响应,形成信息化、智能化、最优化的现代港口。依托“一带一路”战略契机,立足智慧港口建设,以北斗时空为基准,以智能服务为核心,通过CPS信息感知与融合等新一代工业物联网技术,针对“港口协同信息交互”和“多维全景精准引航”等领域开展研发,构建智慧港口时空信息云平台,提高运行效率及安全性,使港口感知更透彻、决策更智能、服务更灵活。在“一带一路”背景下,随着港口转型升级的深入推进,智慧港口作为港口提高运营效能,提升核心竞争力的重要手段,必将大有可为。

个人简介:周毅,男,1981年生,同济大学博士毕业,加拿大滑铁卢大学博士后。现任河南大学副教授,博士生导师,“智能交通协同技术重点研究所”所长,河南省“车联网协同技术国际联合实验室”主任。2016年2月,创办苏州溥诺斯智能科技有限公司,担任执行董事兼CEO,苏州科技创业领军人才。主要研究领域包括:CPS多维感知与融合、协作通信与协同控制、智能交通、智慧港口等。曾先后在美国密歇根大学、日本国立情报学研究所、奥地利电信研究院、中科院上海高研院等研究机构担任访问研究员,参与欧盟第七框架CVIS计划等重大国际合作项目。主持完成国家级科研课题2项,作为核心骨干参与完成国家重大专项、主题863计划、国家自然科学基金重点项目等6项,主持省部级项目、国际合作项目、企业合作项目、国防科技项目等10余项,国家授权发明专利4项。


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