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            智能網絡安全技術研討會

            2019年06月17日  點擊:[]

            會議名稱:智能網絡安全技術研討會

            時 間:2019年6月20日—6月22日

            舉辦單位:大連理工大學

            會議主題:茲通知紐約州立大學石溪分校、中國聯通網絡技術研究院大數據中心、大連理工大學等單位,主要圍繞智能網絡安全現狀進行討論,討論內容如下:1)多媒體數據安全問題;2)移動大數據安全問題; 3)內容中心網絡下安全緩存問題。本次會議將邀請相關研究方向專家學者,報告最新研究成果,對共同感興趣的問題深入交換看法,探尋未來可能的合作研究,同時給相關年輕學者和博士生一個了解當前智能網絡安全現狀及其研究成果的機會。相信此次研討會的召開將促進無線通信環境下智能網絡安全解決方案研究經驗的交流,進一步推動這一領域研究進展。

            日程安排

            620大連理工大學開發區校區綜合樓5樓第一會議室、教學樓401

            9:00 - 9:50

            開幕式 吳國偉(綜合樓5樓第一會議室)

            10:00 - 11:20

            Crossing-Domain Generative Adversarial Networks for Unsupervised Multi-Domain Image-to-Image Translation

            王歆(紐約州立大學)(教學樓A401)

            13:30 - 14:15

            增強學習方案在車載網絡污染攻擊檢測中應用

            陳振宇 (教學樓A401)

            14:20 - 15:10

            分布式學習在移動感知內容中心網絡污染攻擊檢測中應用

            曾宇杰 (教學樓A401)

            15:15 - 16:00

            內容中心網絡下的時間攻擊檢測和防御

            江濱耀 (教學樓A401)

            16:00 - 17:00

            Privacy-PreservingRetrievals inCloudEnvironment

            郭成 (教學樓A401)

            621大連理工大學開發區校區綜合樓5樓第一會議室

            9:00 - 10:30

            運行商大數據體系與應用 程新洲 韓玉輝

            10:30 - 11:30

            內容中心網絡下安全機制概述 姚琳

            13:30 - 14:30

            邊緣網絡中的大數據處理 夏秋粉

            14:35 - 15:35

            未來智能云網絡 徐子川

            15:40 - 16:40

            內容中心網絡下安全協作緩存 李兆洋

            622大連理工大學開發區校區綜合樓

            9:00 - 10:30

            網絡空間安全研究所成果展示 (綜合樓)

            10:30 - 11:30

            參會專家、教師、學生座談 (綜合樓5樓第一會議室)

            校外報告人及相關信息:

            Xin Wang,Dr. Wang is currently the director of the Wireless Networking and Systems lab of the department of Electrical and Computer Engineering of the State University of New York (SUNY) at Stony Brook. She was a Member of Technical Staff in the area of mobile and wireless networking at Bell Labs Research, Lucent Technologies, New Jersey between 2001 and 2003. Dr. Wang has been conducting and leading research work in the design of network architectures, protocols and algorithms. The work of her group falls into a few directions, including advanced wireless network architecture, mobile cloud computing and distributed computing, and big data analysis and deep learning. Dr. Wang obtained her PhD from Columbia University, BS and MS from Beijing University of Post and Telecommunications, respectively. She is a recipient of NSF career award in 2005 and theONRChief of Naval Research (CNR) Challenge award in 2011.

            She currently serves as an associate editor of IEEE Transactions of Mobile Computing (TMC). She also serves TPC chair or program committee members in many technical conferences, including ACM MobiCom, IEEE Infocom, IEEE ICDCS, and IEEE PerCom. Her research group has published more than 100 papers in highly reputed conferences and journals, including ACM Sigmetrics, ACM MobiCom, USENIX NSDI, IEEE ICNP, IEEE Infocom, IEEE ICDCS, IEEE Percom, IEEE TON, IEEE TMC, IEEE JSAC, IEEE TC, and IEEE TDSC.

            報告題目:Crossing-Domain Generative Adversarial Networks for Unsupervised Multi-Domain Image-to-Image Translation

            State-of-the-art techniques in Generative Adversarial Networks(GANs) have shown remarkable success in image-to-image translationfrom peer domain X to domain Y using paired image data.However, obtaining abundant paired data is a non-trivial and expensiveprocess in the majority of applications. When there is aneed to translate images across n domains, if the training is performedbetween every two domains, the complexity of the trainingwill increase quadratically. Moreover, training with data from twodomains only at a time cannot benefit from data of other domains,which prevents the extraction of more useful features and hindersthe progress of this research area. In this work, we propose a generalframework for unsupervised image-to-image translation acrossmultiple domains, which can translate images from domain X toany a domain without requiring direct training between the twodomains involved in image translation. A byproduct of the frameworkis the reduction of computing time and computing resources,since it needs less time than training the domains in pairs as is donein state-of-the-art work. Our proposed framework consists of apair of encoders along with a pair of GANs which learns high-levelfeatures across different domains to generate diverse and realisticsamples from. Our framework shows competing results on manyimage-to-image tasks compared with state-of-the-art techniques.

            程新洲,畢業于北京郵電大學,教授級高級工程師,北京郵電大學兼職教授,研究生導師,IEEE ISCIT、IUCC等國際會議技術委員會主席,教育部學位論文通訊評議專家,中國聯通網絡技術研究院大數據研發中心主任,中國聯通集團專家級戰略人才。主要從事大數據技術研究及應用等工作,近年來主持和參與多項國家及集團級重點項目,項目技術成果先后獲國資委中央企業創新創意大賽優秀獎、工信部/中國信通院大數據星河獎、中國信息協會年度安全可控優秀解決方案獎,以及省部級優秀工程設計、優秀咨詢、創新獎、科學技術獎10余項。在國內外期刊會議發表論文80余篇,專利30余項,出版專著2部。

            韓玉輝,畢業于北京郵電大學,高級工程師,碩士。中國聯通網絡技術研究院大數據研發中心數據解析室負責人,主要從事通信大數據行業應用、移動互聯網DPI技術等研究工作,近年來參與多項國家及集團級重點項目,項目技術成果先后獲工信部/中國信通院大數據星河獎、中國信息協會年度安全可控優秀解決方案獎。在國內外期刊發表論文10余篇,專利20余項,出版專著1部。

            報告題目:運營商大數據體系與應用

            介紹中國聯通數據資源,電信運營商在網絡運行及業務運營方面的數據特征,基于用戶、業務、網絡、終端及內在聯系的電信運營商大數據分析體系,以及該分析體系在企業內部及外部行業的應用實踐。

            上一條:海天學者王歆講座通知 下一條:海外學術大師Dr. M. Jamal Deen報告

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