Description

Students and early-career researchers are warmly invited to special breakfast sessions on Thursday (11-July) and Friday. Sponsored by the Technical Committee on Security and Privacy, the student-organized sessions will explore a new landscape of cyber-physical systems (CPS) research by bringing together young scholars working on the security and privacy of CPS and their applications in diverse areas. In addition to technical presentations, this student-organized workshop features a panel discussion and experience-sharing mixer on academic job-seeking and career development. The primary objective of these sessions is to engage early-career researchers from multiple topical areas in control society and create a vibrant and sustainable research thrust dedicated to the security, privacy, and resiliency of the next-generation cyber-physical systems. 

Slides and templates: Click here

Schedule

11-July-2024 (Thursday)

07:30 to 08:00 - Student presentations (15 minutes each)

08:00 to 08:30 - Panel discussion

12-July-2024 (Friday)

07:30 to 08:00 - Student presentations (15 minutes each)

08:00 to 08:30 - Job seeking mixer

Panel discussion:

The ACC student workshop will host a panel discussion on career development for students and early-career researchers. The panelists include: 

Shaunak D. Bopardikar, Assistant Professor, Michigan State University

Yasser Shoukry, Associate Professor, University of California, Irvine

Sean Warnick, Professor, Brigham Young University

Job seeking mixer:

Join us for an enlightening session tailored for PhD students and recent graduates: "Navigating Post-PhD Careers." This session is designed to demystify the journey from academia to the professional world, providing you with a clear, structured timeline and actionable steps for your career development. Whether you're considering a future in academia, or a research institution, this workshop will offer insights into various career paths, along with the skills and experiences most valued in each domain. We'll discuss how to effectively leverage your PhD experience, highlight crucial job market trends, and identify key resources and strategies for job searching and networking. Participants will also have the opportunity to engage with a panel of successful professionals who have transitioned from PhDs to diverse roles. This interactive session will include Q&A segments, allowing you to address personal concerns and gain tailored advice.

Key takeaways from the job-seeking mixer:

• Understand the timeline for job searching post-PhD.

• Learn about different career paths.

• Access valuable resources and tools for an effective job search.

• Network with peers and professionals to expand your professional circle.

• This workshop is an invaluable resource for those looking to confidently step into their next role post-PhD. Equip yourself with the knowledge and tools to navigate the complex job market and start your career journey with a solid plan in place.

Student presentation:

Luis Burbano, Doctoral student, University of California, Santa Cruz 

Abstract: Cyber-physical systems tightly integrate computational resources with physical processes through sensing and actuating, widely penetrating various safety-critical domains, such as autonomous driving, medical monitoring, and industrial control. Unfortunately, they are susceptible to assorted attacks that can result in injuries or physical damage soon after the system is compromised. Consequently, we require mechanisms that swiftly recover their physical states, redirecting a compromised system to desired states to mitigate hazardous situations that can result from attacks. However, existing recovery studies have overlooked stochastic uncertainties that can be unbounded, making a recovery infeasible or invalidating safety and real-time guarantees. In this talk, I will present a novel recovery approach that achieves the highest probability of steering the physical states of systems with stochastic uncertainties to a target set rapidly or within a given time. Finally, I will demonstrate the practicality of our solution through the implementation in multiple use cases encompassing both linear and nonlinear dynamics, including robotic vehicles, drones, and vehicles in high-fidelity simulators.

Tao Li, Doctoral student, New York University.

Abstract: We present an automated security response for an IT infrastructure and formulate the interaction between an attacker and a defender as a partially observed, non-stationary game. We relax the standard assumption that the game model is correctly specified and consider that each player has a probabilistic conjecture about the model, which may be misspecified in the sense that the actual model has a probability of 0. This formulation allows us to capture uncertainty about the infrastructure and the intents of the players. To learn effective game strategies online, we design a novel method where a player iteratively adapts its conjecture using Bayesian learning and updates its strategy through rollout. We prove that the conjectures converge to best fits, and we provide a bound on the performance improvement that rollout enables with a conjectured model. To characterize the steady state of the game, we propose a variant of the Berk-Nash equilibrium. We present our method through an advanced persistent threat use case. Simulation studies based on testbed measurements show that our method produces effective security strategies that adapt to a changing environment. We also find that our method enables faster convergence than current reinforcement learning techniques.

Dipankar Maity, Assistant professor, University of North Carolina at Charlotte.

Abstract: Privacy in cyber-physical systems has traditionally been achieved through encryption or differential privacy-based methods. However, these methods do not exploit the inherent system-level privacy present in dynamical systems. In this talk, we will explore distributed consensus optimization (DCO) problems under eavesdropping adversaries. We prove that state-of-the-art DCO algorithms are vulnerable to eavesdropping, as adversaries can perfectly learn the algorithm's output by intercepting exchanged messages with any positive probability. While existing literature suggests adding an extra layer of protection, such as encryption or differential privacy techniques, we demonstrate that a simple modification to these algorithms can achieve a certain level of protection without these additional layers. Our modification involves a new inter-node communication protocol: exchanging innovation signals instead of local states. By doing so, we reveal the emergence of a fundamental protection quotient in DCO algorithms. Additionally, we show how the parameters of the DCO algorithms influence the achievable level of protection.

Rijad Alisic, Postdoc, KTH Royal Institute of Technology.

Abstract: In this talk, we will discuss the increasing threats of cyberattacks on Cyber-Physical Systems (CPS) and their potential to devastate our economy, security, and public health. These attacks exploit vulnerabilities in the cyber components of our critical infrastructures, such as sensors and computers, which oversee and control physical processes. Our focus will be on modeling attackers from a defender’s perspective. We propose a novel approach to frame a learning attacker, considering the capabilities it can acquire from the system-generated information, as opposed to specific attackers with an a priori assumed objective. This approach unveils various aspects of an attacker’s learning process and is agnostic about its goals, providing valuable insights for risk analysis about its capabilities. Among these, the system’s privacy emerges as a crucial factor since the attacker relies more on its disclosure resources. Such insights will shed light on the attacker’s learning process and provide guidance on how to fortify our systems against such threats.