Sajal K. Das
生駒 : 奈良先端科学技術大学院大学, 2022.12
Lecture ArchiveOur daily lives are becoming increasingly dependent on smart cyber-physical infrastructures (e.g., smart homes or cities, smart grid, smart transportation, smart healthcare, smart agriculture, etc.). The wide availability of sensor enabled IoT devices and smartphones are also empowering us with fine-grained data collection and opinion gathering via mobile crowdsensing about events of interest, resulting in actionable inferences and decisions. This synergy has led to cyber-physical-human (CPH) convergence in smart living environments, the goal of which is to improve the quality of life. However, CPH and IoT systems are extremely vulnerable to security threats owing to their interdependence, large scale, heterogeneity, human behavior, and trust issues. This talk will highlight unique research challenges in smart living environments, build a unified data falsification threat landscape for CPH and IoT systems, and propose novel anomaly detection frameworks and models for securing such systems. Our novel solutions are based on a rich set of theoretical and practical design principles including AI/ML, data analytics, uncertainty reasoning, information theory, prospect theory, and reputation/ belief models. Case studies with real-world datasets will be presented to secure smart grid and smart vehicular CPS. The talk will be concluded with future research directions.
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2022
電子化映像資料(55分25秒）
情報科学領域・コロキアム ; 2022年度
講演者所属: Department of Computer Science, Missouri University of Science and Technology
講演日: 2022年12月14日 5限
講演場所: 情報科学棟 エーアイ大講義室(L1)
Japan
English (eng)
English (eng)
