{"614048":{"#nid":"614048","#data":{"type":"event","title":"PhD Proposal by Shang-Tse Chen","body":[{"value":"\u003Cp\u003EPh.D. Thesis\u0026nbsp;Proposal\u0026nbsp;Announcement\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003ETitle: AI-infused Security: Robust Defense by Bridging Theory and Practice\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EShang-Tse Chen\u003C\/p\u003E\r\n\r\n\u003Cp\u003EComputer Science PhD Student\u003C\/p\u003E\r\n\r\n\u003Cp\u003ESchool of Computational Science and Engineering\u003C\/p\u003E\r\n\r\n\u003Cp\u003ECollege of Computing\u003C\/p\u003E\r\n\r\n\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022https:\/\/www.cc.gatech.edu\/~schen351\u0022\u003Ehttps:\/\/www.cc.gatech.edu\/~schen351\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDate: Friday, November 16th, 2018\u003C\/p\u003E\r\n\r\n\u003Cp\u003ETime: 11:30am to 1:30pm (EDT)\u003C\/p\u003E\r\n\r\n\u003Cp\u003ELocation: KACB 1123\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003ECommittee:\u003C\/p\u003E\r\n\r\n\u003Cp\u003E----------------\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Polo Chau (Advisor, School of Computational Science and Engineering, Georgia Institute of Technology)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Maria-Florina Balcan (Co-advisor, School of Computer Science, Carnegie Mellon University)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Wenke Lee (School of Computer Science, Georgia Institute of Technology)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Le Song (School of Computational Science and Engineering, Georgia Institute of Technology)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Kevin A. Roundy (Symantec Research Labs)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr.\u0026nbsp;Cory Cornelius (Intel Labs)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EAbstract:\u003C\/p\u003E\r\n\r\n\u003Cp\u003E----------------\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe advances in Artificial Intelligence (AI) has made far-reaching impact in almost every industry. Cybersecurity in particular is one of the important fields that AI has revolutionized. However, while AI has tremendous potential as a defense against real-world cybersecurity threats, understanding the capabilities and robustness of AI remains a fundamental challenge. The goal of this proposed thesis is to develop next-generation strong cybersecurity defenses by uniquely combining techniques from AI, cybersecurity, and algorithmic game theory. Our multi-faceted contributions push the frontiers in each area and the intersection.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThese contributions can be categorized into the following four inter-related research thrusts:\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E(1) \u003Cstrong\u003ETheory-guided Decision Making\u003C\/strong\u003E:\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EWe develop new theories that guide defense resources allocation to guard against unexpected attacks and catastrophic events, using a novel online decision-making framework that compels players to employ \u0026quot;diversified\u0026quot; mixed strategies.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E(2)\u0026nbsp; \u003Cstrong\u003ERobust Distributed Machine Learning\u003C\/strong\u003E:\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EWe develop a communication-efficient distributed boosting algorithm with strong theoretical guarantees in the agnostic learning setting where the data can contain arbitrary noise.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E(3) \u003Cstrong\u003EAdversarial Attack and Defense\u003C\/strong\u003E:\u003C\/p\u003E\r\n\r\n\u003Cp\u003EWe develop ShapeShifter, a physical adversarial attack that fools the state-of-the-art deep-learning-based object detector.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EWe propose to design efficient methods to protect deep neural networks from such kinds of adversarial attacks.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E(4) \u003Cstrong\u003EEnterprise Cyber Threat Detection\u003C\/strong\u003E: We show how AI can be used in real enterprise environment by designing a novel framework called \u0026quot;Virtual Product\u0026quot; to predict potential enterprise cyber threats from telemetry data.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThis thesis research will make multiple important contributions.\u0026nbsp; First, it improves people\u0026#39;s understanding of the capabilities and limitations of AI under adversarial circumstances. Second, it offers guiding principles for future research on how to empower security-critical applications by effectively combining AI, cybersecurity, and algorithmic game theory. Third, our machine learning algorithms are scalable and general, and thus can be used in a wide range of applications.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"AI-infused Security: Robust Defense by Bridging Theory and Practice"}],"uid":"27707","created_gmt":"2018-11-08 20:45:49","changed_gmt":"2018-11-08 20:45:49","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2018-11-16T11:30:00-05:00","event_time_end":"2018-11-16T13:30:00-05:00","event_time_end_last":"2018-11-16T13:30:00-05:00","gmt_time_start":"2018-11-16 16:30:00","gmt_time_end":"2018-11-16 18:30:00","gmt_time_end_last":"2018-11-16 18:30:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}