UIUC SoS Lablet Quarterly Executive Summary - October 2019

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.

[Project: An Automated Synthesis Framework for Network Security Resilience] We continued our initiative to perform automatic synthesis of fault tolerance in SDNs. Given the scale of computer networks today, failures are the norm rather than an exception, making failure tolerance a key requirement. Moreover, in order to minimize data loss during failures, it is ideal that failures are handled in the data plane rather than the control plane. Both traditional networks, as well as SDNs, have mechanisms in the data plane to respond quickly to failures. In Openflow, the mechanism is provided by Fast Failover groups, which monitor the status of ports and appropriately redirect traffic around unavailable ones. However, leveraging Fast Failover groups for fast fault tolerance adds an extra dimension of complexity to the already challenging task of developing an SDN control application. We are working on a technique and a tool that automatically and transparently implements Fast Failover for Openflow programs, requiring no perturbation to the control plane.  Our current prototype pre-computes backup paths for failures and installs them as MPLS tunnels in the data plane. It also configures Fast Failover groups to use these tunnels to reroute traffic in the event of failures. Finally, it transparently intercepts and alters the communication between the control plane and the data plane to use the Fast Failover groups. Our prototype shows that our technique is effective on test cases. We are currently working on extending this prototype into a practical system. We will then perform an emulation on test data and collect results.

[Project: A Monitoring Fusion and Response Framework to Provide Cyber Resiliency] Our RRE work incorporates modules to monitor current state of a system, detect intrusions, and respond to achieve resilience-specific goals.  Intrusion detection in large-scale distributed systems, which is a necessary precondition for intrusion tolerance and resilience, is highly susceptible to malicious manipulation of system data used for detection (e.g., using rootkits and log tampering), which we term “monitor compromise”. Existing literature attempts to counteract the problem using reputation systems, which weight the trustworthiness of monitor data based on past trustworthiness of the data, but such systems are themselves subject to “betrayal attacks” and “sleeper attacks”. We instead propose the use of data-driven methods for detecting potential monitor compromise. We leverage the insight that systems usually contain multiple monitors that provide redundant information about system activity, so we can use discrepancies between observations of system activity across different monitors to identify potential monitor compromise.

[Project: Uncertainty in Security Analysis] We are analyzing real system logs in order to come up with ways to quantify the probability that an attacker can perform lateral movement across a certain link in the network.

We are developing our own risk assessment framework for SCADA systems; at the same time, we are looking for an efficient way to estimate the risk measure using Monte Carlo simulations.

[Project: Resilient Control of Cyber-Physical Systems with Distributed Learning] We have developed and implemented a nearly sample-optimal algorithm for statistical model checking of markov decision processes. This advances the state of the art in achieving resiliency (hard problem) as optimal data usage for verification makes the algorithms effective for offline analysis of autonomous system design as well as on board monitoring.

We have developed a collection of benchmarks for comparing our approach with existing model checking tools such as Prism, Storm, and Plasma Lab that are also used for security and resiliency analysis of autonomous and cyber-physical systems.

[Project: A Human-Agent-Focused Approach to Security Modeling] Work was suspended the first six weeks of the quarter, as the researcher, Michael Rausch, was away for an internship at Sandia National Laboratories. Since then, work has been focused on developing a novel metamodeling-based technique for faster sensitivity analysis and uncertainty quantification in security models, particularly GAMES models. We find this a particularly important problem, given that all of the inputs to security models are rarely known with certainty (motivating sensitivity analysis and uncertainty quantification), and the slow speed of execution makes it difficult to perform a thorough analysis of the inputs on the original model. By creating a metamodel that trades some accuracy for speed we can perform a more thorough analysis of the input space.  Background research was conducted, and a prototype tool that utilizes the approach is currently under development.

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

Publications

1. Shangyu Xie, Han Wang, Shengbin Wang, Haibing Lu, Yuan Hong, Dong Jin and Qi Liu, Discovering Communities for Microgrids with Spatial-Temporal Net Energy, Journal of Modern Power Systems and Clean Energy (MPCE), July 2019

2. Santhosh Prabhu, Kuan Yen Chou, Ali Kheradmand, Brighten Godfrey, Matthew Caesar, Plankton: Scalable Network Configuration Verification Through Model Checking, NSDI, February 2020

3. U. Thakore, H. V. Ramasamy, W. H. Sanders, “Coordinated Analysis of heterogeneous Monitor Data in Enterprise Clouds for Incident Response,” to appear in the 30th International Symposium on Software Reliability Engineering (ISSRE 2019).

4. C. Cheh, U. Thakore, B. Chen, W.G. Temple, and W.H. Sanders, “Leveraging Physical Access Logs to Identify Tailgating: Limitations and Solutions”, to appear in European Dependable Computing Conferences

5. Sample-optimal Verifiction of Markov Decision Processes, Musavi, Sun, Mitra, Shakkottai, and Dullerud, October 2019.

6. Verifying PCTL Specifications on Markov Decision Processes via Reinforcement Learning, Yu Wang, Nima Roohi, Matthew West, Mahesh Viswanathan and Geir Dullerud, submitted to 21^st International Conference on Verification, Model Checking (VMCAI); in review 2019.

7. Data-driven safety verification of complex cyber-physical systems, Chuchu Fan and Sayan Mitra. A chapter in the book titled Design Automation for Cyber-Physical Systems, edited by Mohammad Abdullah Al Faruquqe and Arquimedes Canedo, pages 107-143, Springer, 2019.

8. Using symmetry transformations in equivariant dynamical systems for their safety verification Hussein Sibai, Navid Mokhlesi and Sayan Mitra; accepted for publication in the proceedings of the Seventeenth International Symposium on Automated Technology for Verification and Analysis (ATVA), October 28-31, 2019, Taipei City, Taiwan. Nominated for best paper award

9. Hoang Hai Nguyen, Kartik Palani, and David M. Nicol, "Extensions of Network Reliability Analysis", 49th IEEE/IFIP International Conference on Dependable Systems and Networks (DSN 2019), Portland, OR, June 24-27, 2019.

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 and Kyle Hale are developing a new graduate-level cyber security laboratory class “System and Network Security” targeting release for Spring 2020 at Illinois Institute of Technology. 
   
• Kevin Jin has been appointed as the Director of the new Master of Cybersecurity Program in the College of Science at Illinois Institute of Technology (https://science.iit.edu/programs/graduate/master-cybersecurity-mcybcode).  The program will serve as one more platform to disseminate the educational and research outcomes of our Science of Security projects.
   
• Kevin Jin and Chen Chen (Argonne National Lab) are preparing a tutorial titled "Electric Power System Resilience" at the 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) in October 2019.
   
• We organized a Ph.D. colloquium as part of the ACM SIGSIM-PADS conference in June 2019. The Ph.D. colloquium include a career panel, poster session, student presentations, and a meeting with editors. We received 20+ submissions and 15 students were selected to present their work, among which 5 US-based Ph.D. students received the NSF student travel grant. The event has provided mentoring and educational opportunities to the young researchers, thus contributing to equipping them with tools that support their career success.
   
• Kevin Jin gave a full-day tutorial on “Cyber Security and Resilience of Cyber-Physical Systems” in the Internet of Things (IoT) Systems Research Center at the University of Wisconsin Madison, June 2019
   
• Matthew Caesar has created a new class on Internet of Things at UIUC. The class contains extensive coverage of security in this important domain. The class is slated for public release this fall on Coursera’s Massive Online Open Course (MOOC) platform. The course will be open for enrollment by anyone, even people not attending the University of Illinois. Most lecture content and labs have been created and the course is approximately 85% filmed. We are currently working on an autograding infrastructure – when completed the class will be fully automated, allowing large numbers of students to enroll and learn advanced concepts in IoT security.
   
• Matthew Caesar also continues to refine his Networking Laboratory class, targeting release for Spring 2020. He has developed a new set of Cybersecurity lectures for his class, covering important topics, and educating students how to improve security of common networking deployments.
   
• Matthew Caesar is currently constructing an online platform for working with IoT devices in the cloud. The platform virtualizes IoT devices, internally leveraging a new technology that extends virtual machines into the IoT domain. This work will probably take another year to develop, but when it is released, we hope to grow from small pilots to a platform that can allow students across the world to learn about and work with IoT security in a manner that greatly accelerates their ability to experiment and learn.
• PI Mitra’s new course Principles of Safe Autonomy at University of Illinois came to a successful conclusion in May. The course takes a deep dive into the seminal topics in object recognition, learning, localization, decision making, path planning, control, and safety verification. 25 students from ECE and CS are completed the course. The course team has designed 6 New programming assignments involving topics such as lane detection, road-sign recognition with deep neural networks, localization with particle filters, decision making with reinforcement learning, path planning with rapidly expanding random trees, and safety verification using simulation-driven proofs. The students used a high-fidelity, commercial-grade vehicle simulator (Righthook) for testing their programming assignments. Galois Inc. sponsored prizes for student projects. Find out more about the safe autonomy course and the student projects at https://publish.illinois.edu/safe-autonomy/
• Carmen Chen successfully defended her PhD thesis on June 4^th, and is a postdoctoral researcher at Singapore University of Design and Technology.