Visible to the public VU SoS Lablet Quarterly Executive Summary - JUL 2022Conflict Detection Enabled


Executive Summary Report

  1. Fundamental Research

    High level report of result or partial result that helped move security science forward-- In most cases it should point to a "hard problem". These are the most important research accomplishments of the Lablet in the previous quarter.

    The Science of Security for Cyber-Physical Systems (CPS) Lablet focuses on (1) Foundations of CPS Resilience, (2) Analytics for CPS Cybersecurity, (3) Development of a Multi-model Testbed for Simulation–based Evaluation of Resilience, (4) Mixed Initiative and Collaborative Learning in Adversarial Environments, and (5) Cyber Makerspace for CPS security. 
     
    • As the popularity of graphics processing units (GPUs) grows rapidly in recent years, it becomes very critical to study and understand the security implications imposed by them. We show that modern GPUs can “broadcast” sensitive information over the air to make a number of attacks practical. Specifically, we present a new electromagnetic (EM) side-channel vulnerability that we have discovered in many GPUs of both NVIDIA and AMD. We show that this vulnerability can be exploited to mount realistic attacks through two case studies, which are website fingerprinting and keystroke timing inference attacks. Our investigation recognizes the commonly used dynamic voltage and frequency scaling (DVFS) feature in GPU as the root cause of this vulnerability. Nevertheless, we also show that simply disabling DVFS may not be an effective countermeasure since it will introduce another highly exploitable EM side-channel vulnerability. To the best of our knowledge, this is the first work that studies realistic physical side-channel attacks on non-shared GPUs at a distance.
       
    • Despite the rapid development of cybersecurity, recovery of the operation of the impacted CPS after a cyber-attack, as a core element of cyber resilience, is often left to human decision-makers. There is a high demand for an autonomous intelligent cyber defense agent (AICA) for planning a rapid recovery. In this work, we introduce and demonstrate a system for recovery planning using simulation-based predictive monitoring to recover the system from attacks (cyber, physical, or hardware) and disruptions automatically. The recovery planning system first evaluates the impact of system degradation and generates courses of actions (COAs) for recovery efficiently. Then, it evaluates these COAs through integrated heterogeneous simulations that accounts for unavoidable uncertainty. By formalizing security and safety requirements, it formally verifies recovery COAs with confidence guarantees, and obtains the optimal recovery COAs. We present two recovery scenarios in smart cities to demonstrate the effectiveness of our recovery planning system.
       
  2. Community Engagement(s)
    • Research interaction in the community including workshops, seminars, competitions, etc.
       
    • Presentations and discussion with National Security Staff for TN Sen. Marsha Blackburn & TN Sen. Bill Hagerty on Research topics of wireless/RF security and resilience, June 28, 2022.
       
    • Our research was presented in the following conference: IEEE Symposium on Security and Privacy (IEEE S&P 2022), American Control Conference (ACC 2022), Mediterranean Conference on Control and Automation (MED 2022), and HoTSoS Symposium 2022.
       
    • Collaboration with NIST on threat modeling and risk analysis in industrial control systems.