Andrew Clark
AFOSR YIP: Leios: Complex Network Resilience Through Controlled Islanding and Reconnection
Interdependent failures, in which the loss of one node or edge causes neighboring nodes or edges to fail, can create cascades that lead to widespread network outages. High-profile examples include power system blackouts and malware/epidemic propagation in cyber, social, and biological networks. One approach to mitigating interdependent failures in defense, social, and infrastructure applications is islanding, in which a set of edges is deliberately removed after a disturbance in order to partition the network. This project will research and develop Leios, a framework for adaptive, agile, and resource-efficient controlled islanding and reconnection with minimal service disruption. Our framework will enable network nodes to compute and execute islanding and reconnection in a distributed manner. This distributed approach will improve scalability of the island computation while making islanding robust to changing network conditions and unexpected disruptions due to malicious attacks, and will improve resilience of applications including power grids, cyber systems, and social networks. The effort builds on the PI’s extensive track record in complex networks, optimization, game theory, and networked control systems.
Sponsor: Air Force Office of Scientific Research Young Investigator Program (AFOSR YIP)
NSF CAREER: Synthesis and Control of Cyber-Resilient CPS
This project will develop design methodologies for the synthesis of cyber-physical systems (CPS) that verifiably satisfy given safety and performance requirements when an unknown set of system components is compromised. The need for such design methodologies is exemplified by recent intrusions into nuclear facilities and ransomware attacks on municipal governments, in which adversaries found weak points in cyber defenses that were leveraged to control safety-critical physical infrastructures. Our research plan is grounded on two application scenarios: (i) a group of unmanned vehicles that must complete high-level task objectives while avoiding collisions in the presence of false and malicious sensor and control inputs, and (ii) a smart building in which IoT apps send malicious commands to the building HVAC and other safety-critical systems.
Sponsor: National Science Foundation (NSF)
Project Website: Link
HYDRA: A Submodular Optimization Framework for Dynamic Network Resilience in the Presence of Antagonistic Interactions and Interdependent Failures
This project will research and develop a submodular optimization framework for resilient complex networks. Submodularity is a diminishing returns property that enables the development of computationally efficient algorithms with provable optimality bounds. Based on our extensive prior work on submodularity in network dynamics and control, we believe that many of the key metrics quantifying network stability, performance, and resilience have a submodular structure. Since complex networks contain both discrete and continuous design elements, we investigate discrete as well as continuous submodularity (DR-submodularity). Our proposal highlights the need to define new notions of submodularity (hybrid submodularity) for optimization of joint continuous-discrete functions in complex networks.
Sponsor: Air Force Office of Scientific Research (AFOSR)
Collaborators: University of Washington (lead)
Project Website: Link
Smart Technologies and Community Engagement to Address Data Gaps in Birth Outcomes Reporting
This one-year planning project, a collaboration between Arizona State University, Worcester Polytechnic Institute, and the Birthworkers of Color Collective (BCC), investigates technology usage in a community of birthworkers in Long Beach, CA to imagine how smart technologies could improve the collection, quality and accuracy of data that is collected through the Los Angeles Mommy and Baby (LAMB) survey. Funding will support four workshops that bring together the research team and BCC doulas to establish shared knowledge, understanding, and vision. The workshop activities for this grant will be informed by fusing expertise in design justice framework and systems engineering modeling, in an effort to evaluate technology use and data and privacy issues, with a sensitivity to historical trauma and distrust of research in an underserved population.
Sponsor: National Science Foundation (NSF)
Collaborators: Arizona State University (lead)
Project Website: Link
L2RAVE: Feedback-Driven Learn to Reason in Adversarial Environments for Autonomic Cyber Systems
The growing complexity of cyber systems has made them difficult for human operators to defend, particularly in the presence of intelligent and resourceful adversaries who target multiple system components simultaneously, employ previously unobserved attack vectors, and use stealth and deception to evade detection. There is a need for developing autonomic cyber systems that can integrate statistical learning and rules-based formal reasoning to provide an adaptive and robust situational awareness and resilient system response. In this collaborative research effort, we propose to develop a feedback-driven Learn to Reason (L2R) framework, which aims to integrate statistical learning with formal reasoning, in adversarial environments. Our insight is that in order to realize the potential benefits of L2R, continuous interaction between the statistical and formal components is needed, both at intermediate time steps and at multiple layers of abstraction.
Sponsor: Office of Naval Research (ONR)
Collaborators: University of Washington (lead)
Project Website: Link
ONR MURI ADAPT: Analytical Framework for Actionable Defense Against Advanced Persistent Threats
I am a collaborating faculty with this MURI, whose aim is to develop a new encompassing scientific framework with a novel games underpinning that tightly incorporates real-world parameters and observations for analytically representing adversarial cyber interactions. Advanced persistent threats (APTs) infiltrate cyber systems over an extended period of time and compromise specifically targeted data and/or resources through a sequence of stealthy attacks, and often have multiple variants. Currently, there is no scientific framework to represent APTs, understand the effectiveness of cyber defenses, or develop an actionable cyber defense. The scientific framework we develop will provide a formal and predictive mathematical language for representing the temporal progression of the adversarial cyber interaction.
Sponsor: Office of Naval Research (ONR)
Collaborators: University of Washington (lead), Georgia Institute of Technology, University of Illinois - Urbana-Champaign, University of California-Berkeley, University of California-Santa Barbara
Project Website: Link