Dr. Sajal Das
Daniel St. Clair Endowed Chair, Dept. of Computer Science
Missouri University of S&T

Our daily lives are becoming increasingly dependent on a variety of IoT-enabled, cyber-physical-human (CPH) systems (e.g., smart cities, smart grid, smart transportation, smart healthcare, and smart agriculture), the goal of which is to improve the quality of life. However, CPH and IoT systems are extremely vulnerable to security (adversarial) threats posing unique research challenges. This talk will propose novel frameworks and models for securing CPH and IoT systems, based on a rich set of theoretical and practical design principles, such as machine learning, data analytics, uncertainty reasoning, information theory, prospect theory, reputation scoring, and trust models. Two case studies will be considered. The first one aims to design security forensic solutions and lightweight anomaly detection in smart grid CPS to defend against organized and persistent threats that can launch data integrity attacks on smart meters using stealthy strategies. The novelty of this approach lies in a newly defined information-theoretic metric that quantifies robustness and security, minimizing attacker’s impact and false alarm rates. The second case study deals with secure and trustworthy decision making in mobile crowd sensing based vehicular CPS to detect false or spam contributions due to users’ selfish and malicious behaviors. Based on the cumulative prospect theory and reputation/trust model, our approach prevents revenue loss owing to undue incentives and improves operational reliability and decision accuracy. The talk will be concluded with directions of future research.

Please note that for all in-person events, attendees must adhere to Washington University’s public health requirements, including the latest events and meetings protocol. Guests will be required to show a successful self-screening result and wear a mask at all times. 

Dr. Sajal Das
Daniel St. Clair Endowed Chair, Dept. of Computer Science
Missouri University of S&T

Our daily lives are becoming increasingly dependent on a variety of IoT-enabled, cyber-physical-human (CPH) systems (e.g., smart cities, smart grid, smart transportation, smart healthcare, and smart agriculture), the goal of which is to improve the quality of life. However, CPH and IoT systems are extremely vulnerable to security (adversarial) threats posing unique research challenges. This talk will propose novel frameworks and models for securing CPH and IoT systems, based on a rich set of theoretical and practical design principles, such as machine learning, data analytics, uncertainty reasoning, information theory, prospect theory, reputation scoring, and trust models. Two case studies will be considered. The first one aims to design security forensic solutions and lightweight anomaly detection in smart grid CPS to defend against organized and persistent threats that can launch data integrity attacks on smart meters using stealthy strategies. The novelty of this approach lies in a newly defined information-theoretic metric that quantifies robustness and security, minimizing attacker’s impact and false alarm rates. The second case study deals with secure and trustworthy decision making in mobile crowd sensing based vehicular CPS to detect false or spam contributions due to users’ selfish and malicious behaviors. Based on the cumulative prospect theory and reputation/trust model, our approach prevents revenue loss owing to undue incentives and improves operational reliability and decision accuracy. The talk will be concluded with directions of future research.

Please note that for all in-person events, attendees must adhere to Washington University’s public health requirements, including the latest events and meetings protocol. Guests will be required to show a successful self-screening result and wear a mask at all times.