Visible to the public UIUC SoS Lablet Quarterly Executive Summary - October 2021Conflict Detection Enabled

A. 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.


A Monitoring Fusion and Response Framework to Provide Cyber Resiliency

This quarter we presented our findings regarding the applicability of metamodeling at QEST 2021, and our work was published in the conference proceedings. We received valuable feedback from the conference participants, and the audience also seemed to appreciate our work. We developed a plan to make the metamodeling tool more widely available. The last graduate student working on this project just graduated, so this will be the final report for this project.


Uncertainty in Security Analysis


We consider time as an additional dimension of network security analysis. Our goal is to capture and incorporate network changes into the existing framework so that analyses can be performeds in an accurate and timely fashion. Current effort focuses on understanding changes associated with an ongoing attack, including the uncertainty about the current and future states of the attack and its implication on incident detection and response.



An Automated Synthesis Framework for Network Security and Resilience

  • We published one paper in FlexNets 2021. A book chapter “Dynamic Data-Driven Approach for Cyber Resilient and Secure Critical Energy Systems” has been accepted and will appear in the Handbook on Dynamic Data Driven Application Systems (DDDAS) Vol. II.
  • We continue to study the interdependence between the power system and the communication network to improve resilience in critical energy infrastructures, which addresses the resilient architecture hard problem. In the current quarter, we conducted a literature review of recent publications on power-communication interdependency and the impact on system resilience. We revised the current restoration optimization model and the network generation algorithm. We also analyzed the existence of solutions of the updated optimization model. We identified that the model works well for most cases but may not generate a solution in time for certain large-scale systems. Currently, we are investigating ways to effectively reduce the problem size. Finally, we submitted a revision of the paper describing this work to the IEEE Transactions of Smart Grid.
  • We continue to develop a simulation-based platform for cyber-physical system resilience and security evaluation, which addresses the resilient architecture and scalability hard problem. In the current quarter, we discovered through experiments that the existing virtual time system lacks proper control of process waiting time. The current design affected not only the disk I/O time, but also the network I/O time and the GPU computational time. To address the problem, we proposed a compensation mechanism and modified the Linux kernel to precisely control time advancement not only during execution burst by also during waiting time. We are conducting experiments for error analysis. We are also working on a large-scale case study to demonstrate the effectiveness of the updated virtual time system. Finally, we are preparing a manuscript describing this work targeting the 2022 ACM SIGSIM-PADS conference.
  • We have developed a design and evaluation framework for a self-driving “service provider infrastructure” that leverages our prior work on verification and synthesis to automatically self-configure to become resilient to attacks. Our initial focus Is on network and container orchestration systems, and our first implementation will target Kubernetes. Our platform leverages AI planning algorithms to synthesize steps the system needs to take to protect itself against incoming attacks from an intelligent adversary.


Resilient Control of Cyber-Physical Systems with Distributed Learning


We are developing safety and security analysis approaches for real-life of autonomous and cyber-physical systems using statistical and machine learning techniques. Our approaches rely on distributed and sample-efficient optimization techniques that have been developed in the context of the Multi-armed bandit problem. We have shown how these optimization algorithms can be used effectively for statistical model checking of markov decision processes and hybrid systems. We have built a suite of benchmarks related to online safety analysis of autonomous and semi-autonomous vehicles. Our initial results are very promising as the data usage and the running time of our algorithms can be several orders of magnitude better than existing model checking approaches such as Storm and Prism. Two PhD students are dedicating their research time to the project and the prototype tool has been made available online.



B. Community Engagement(s)
Research interaction in the community including workshops, seminars, competitions, etc.

  • Matthew Caesar will serve as the Sponsor Chair for ACM SIGCOMM 2022.
  • Matthew Caesar will serve on the Program Committee for USENIX NSDI 2022.
  • Matthew Caesar served as a Juror in the ACM SIGCOMM Student Research Competition, 2021
  • Matthew Caesar was elected as the Vice Chair for ACM SIGCOMM, and will serve a four-year term. In his position, he will be responsible for leading initiatives in the SIGCOMM community, with an emphasis on education and cybersecurity.
  • Matthew Caesar served as an invited panelist in the 39th Brazilian Symposium on Computer Networks and Distributed Systems (2021)
  • Kevin Jin will serve as the Program Co-chair for ACM SIGSIM-PADS conference in 2022.
  • Kevin Jin will serve as a panelist in the “Dynamic Data-Driven Application Systems” track at the 2021 INFORMS annual conference.
  • Sayan Mitra is serving as the General Chair of HoTSoS 22.
  • Sayan Mitra participated in virtual roundtable on “Formal methods for cyber-physical systems”, appeared in the IEEE Computer magazine, 2021.




Evaluating the Effectiveness of Metamodels in Emulating Quantitative Models. M. Rausch and W.H. Sanders.Proceedings of the International Conference on Quantiative Evaluating of SysTems (QEST), Paris, France, August 23-27, 2021.


Verification and Parameter Synthesis for Stochastic Systems using  Optimistic Optimization, Negin Musavi, Dawei Sun, Sayan Mitra, Sanjay Shakkottai, and Geir Dullerud, to appear in Proceedings of IEEE Conference on Control Technology and Applications (CCTA), September 2021.

Continuous Integration and Testing for Autonomous Racing Software: An Experience Report from GRAIC, Minghao Jiang, Kristina Miller, Dawei Sun, Zexiang Liu, Yixuan Jia, Arnab Datta, Necmiye Ozay and Sayan Mitra. Contributed paper in ICRA 21 Workshop on Opportunities and Challenges with Autonomous Racing, 31 May, 2021.

Egocentric abstractions for verification of distributed cyber-physical systems. Sung Woo Jeon and Sayan Mitra. IEEE Workshop on the Internet of Safe Things (SafeThings'21), co-located with Oakland, 2021. Won the Best Paper Award.

NeuReach: Learning Reachability Functions from Simulations, Dawei Sun and Sayan Mitra, in preparation, September 2021.

Policy Optimization for Markovian Jump Linear Quadratic Control: Gradient-Based Methods and Global Convergence and Parameter Synthesis for Stochastic Systems using  Optimistic Optimization, Joao Jansch-Porto, Bin Hu, and Geir Dullerud, submitted for review, January 2021.

MLEFlow: Learning from His- tory to Improve Load Balancing in Tor, H. Darir, H. Sibai, C.-Y. Cheng, N. Borisov, G.E. Dullerud, and S. Mitra, accepted to Privacy Enhancing Technologies Symposium(PETS), 2022.

Verifying Stochastic Hybrid Systems with Temporal Logic Specifications via Model Reduction, Yu Wang, Y., Nima Roohi, Matt West, Mahesh Viswanathan, and Geir Dullerud,  to appear in Transactions on Embedded Computing Sys- tems, May 2021.

Linear Bandit Algorithms with Sublinear Time Complexity, Shuo Yang, Tongzheng Ren, Sanjay Shakkottai, Eric Price, Inderjit Dhillon and Sujay Sanghavi, submitted for review, February 2021.



C. Educational Advances
Impact to courses or curriculum at your school or elsewhere that indicates an increased training or rigor in security research.

  • Kevin Jin is developing a new graduate-level network security class for the University of Arkansas Global Campus. The class will be offered in Spring 2022.