Secure Decentralized Inference and Learning in IoT-type Networks

Presented by:
Soummya Kar, Ph.D.
Professor of Electrical and Computer Engineering
at Carnegie Mellon University

Organized by
Lehigh University’s Institute for Cyber Physical Infrastructure and Energy (I-CPIE)

1:00 pm EST
July 1, 2020
Via Zoom

Abstract
In applications such as large-scale cyber-physical systems (CPS) and Internet-of-Things (IoT), as the number of devices or agents continues to grow, the integrity and trustworthiness of data generated by these devices becomes a pressing issue of paramount importance. An adversary may hijack individual devices or the communication channel between devices to maliciously alter data streams. In numerous IoT applications, we deploy physical devices throughout an environment, and we are interested in using the stream of sensor measurements to facilitate learning and inference tasks. Due to the large-scale and distributed nature of devices and data it might be infeasible to carry out computation and decision-making in a classical centralized fashion as well as to prevent attacks and intrusions on all data sources. As a result, countermeasures such as intrusion detection schemes and resilient information processing algorithms become a vital component of security in distributed IoT-type setups. In this talk we focus on decentralized network-based architectures in which devices themselves perform the necessary computations using local data and peer-to-peer information exchange with neighboring devices to make inferences about an environment. In the first part of the talk, we review distributed inference and learning approaches and algorithms based on the consensus+innovations paradigm. We discuss performance metrics such as rates of convergence, communication complexity, and optimality. The second part of the talk focuses on recent work on secure and resilient variants of these algorithms in adversarial environments. Specifically, focusing on the case of data integrity attacks on the device network, we characterize fundamental trade-offs between resilience, quantified in terms of achievable inference performance and ability to detect intrusions and threats, and model properties such as observability and connectivity of the inter-device communication network.

Biosketch
Soummya Kar received a B.Tech. in electronics and electrical communication engineering from the Indian Institute of Technology, Kharagpur, India, in May 2005 and a Ph.D. in electrical and computer engineering from Carnegie Mellon University,
Pittsburgh, PA, in 2010. From June 2010 to May 2011, he was with the Electrical Engineering Department, Princeton University, Princeton, NJ, USA, as a Postdoctoral Research Associate. He is currently a Professor of Electrical and Computer Engineering at Carnegie Mellon University, Pittsburgh, PA, USA. His research interests include decision-making in large-scale networked  systems, stochastic systems, multi-agent systems and data science, with applications to cyber-physical systems and smart energy systems. Recent recognition of his work includes the 2016 O. Hugo Schuck Best Paper Award from the American Automatic Control Council and a 2016 Dean's Early Career Fellowship from CIT, Carnegie Mellon.