Secure and Resillient Networked Systems

As large scale and networked technical infrastructures become more common, we must ensure that they can operate robustly and with resillience in the face of model uncertainties or even antagonistic attacks. Our research explores novel frameworks and methodologies for designing secure and resilient multi-agent systems. Our research takes a proactive approach, aiming to develop inherently secure systems with built-in breach detection mechanisms and countermeasures.

We adopt a systematic approach to incorporate security considerations into the fundamental design principles of networked dynamical systems, creating systems that are secure by design. Our research encompasses the development of analytical tools, algorithms, and frameworks that facilitate the design and analysis of secure multi-agent systems, enhancing their resilience against various types of attacks and ensuring their ability to accomplish their intended tasks reliably. We also develop analysis tools to help quantify the robustness, or vulnerability, of the network system to attacks.

Secure consensus Network robustness Structural rank

Related Publications:

  1. M.-A. Belabbas, X. Chen, and D. Zelazo, “On Structural Rank and Resilience of Sparsity Patterns,” IEEE Transactions on Automatic Control, 68(8):4783–4795, 2023.
    Belabbas2021a_J.pdf DOI: 10.1109/tac.2022.3212013
  2. M. Fabris and D. Zelazo, “A Robustness Analysis to Structured Channel Tampering Over Secure-by-Design Consensus Networks,” IEEE Control Systems Letters, 7:2011–2016, 2023.
    Fabris2023_J.pdf Fabris2023_J.slides DOI: 10.1109/LCSYS.2023.3284482
  3. M. Sharf and D. Zelazo, “Cluster assignment in multi-agent systems: Sparsity bounds and fault tolerance,” Asian Journal of Control, 2023.
    Sharf2023b_J.pdf DOI: 10.1002/asjc.3149
  4. M. Fabris and D. Zelazo, “Secure Consensus via Objective Coding: Robustness Analysis to Channel Tampering,” IEEE Transactions on Systems, Man and Cybernetics: Systems, 52(12):7885–7897, 2022.
    Fabris2022a_J.pdf DOI: 10.1109/tsmc.2022.3177756
  5. D. Muhkerjee and D. Zelazo, “Robustness of Consensus over Weighted Digraphs,” IEEE Transactions on Network Sciences and Engineering, 6(4):657–670, 2019.
    Muhkerjee2017a_J.pdf DOI: 10.1109/tnse.2018.2866780
  6. D. Muhkerjee and D. Zelazo, “Consensus of Higher Order Agents: Robustness and Heterogeneity,” IEEE Transactions on Control of Network Systems, 6(4):1323–1333, 2019.
    Muhkerjee2017b_J.pdf DOI: 10.1109/tcns.2018.2889003
  7. D. Mukherjee and D. Zelazo, “Robust Consensus of Higher Order Agents over Cycle Graphs,” in 58th Israel Annual Conference on Aerospace Sciences, Haifa, Israel, Feb. 2018.
  8. D. Zelazo and M. Bürger, “On the Robustness of Uncertain Consensus Networks,” IEEE Transactions on Control of Network Systems, 4(2):170–178, 2017.
    Zelazo2014a_J.pdf DOI: 10.1109/tcns.2015.2485458
  9. D. Mukherjee and D. Zelazo, “Robust Consensus of Higher Order Agents over Cycle Graphs,” in 56th Israel Annual Conference on Aerospace Sciences, Haifa, Israel, Mar. 2016.
    Mukherjee2016a.slides
  10. D. Mukherjee and D. Zelazo, “Consensus Over Weighted Digraphs: A Robustness Perspective,” in 55th IEEE Conference on Decision and Control, Las Vegas, Nevada, Dec. 2016.
    Mukherjee2016b.pdf DOI: 10.1109/cdc.2016.7798784