Topics

Secure and Resillient Networked Systems

Large-scale networked systems must continue to operate despite uncertainty, faults, communication failures, and malicious interference. Security and resilience therefore need to be part of the control architecture, not only an after-the-fact detection layer.

Our work studies secure-by-design consensus, robustness of networked dynamics, structural resilience, and fault-tolerant graph properties. The emphasis on this page is the robustness and security layer; related consensus and network-identification methods are handled on their own pages when that is the primary contribution.

Secure-by-Design Consensus

Secure-by-design consensus modifies the information exchanged over the network so that channel tampering can be detected or mitigated by the protocol structure itself. Instead of treating attacks as external disturbances, the controller architecture encodes redundancy and objective information directly into the consensus process.

Our work develops objective-coding methods and robustness analysis tools for structured channel tampering in consensus networks.

Secure consensus architecture
Secure-by-design consensus architecture for detecting structured channel tampering.

Representative Publications:

  1. 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 Fabris2023_J.bibtex
  2. 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 Fabris2022a_J.bibtex

Robust Consensus and Network Uncertainty

Robust consensus asks how graph weights, uncertainty, heterogeneity, and higher-order agent dynamics affect the ability of a network to reach agreement. These questions are especially important when the communication graph is weighted, directed, or only approximately known.

This work develops robustness measures, uncertainty analysis, and graph-dependent tools for consensus networks under model mismatch and heterogeneous dynamics.

Effective-resistance robustness measure for uncertain consensus networks
Graph-theoretic robustness measures for uncertain consensus networks.

Representative Publications:

  1. D. Mukherjee 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 Muhkerjee2017a_J.bibtex
  2. D. Mukherjee 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 Muhkerjee2017b_J.bibtex
  3. D. Mukherjee and D. Zelazo, “Robust Consensus of Higher Order Agents over Cycle Graphs,” in 58th Israel Annual Conference on Aerospace Sciences, Haifa, Israel, Mar. 2018.
    Mukherjee2016a.pdf Mukherjee2016a.slides Mukherjee2016a.bibtex
  4. 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 Zelazo2014a_J.bibtex
  5. 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 Mukherjee2016b.bibtex

Structural Resilience and Fault Tolerance

Some resilience questions depend mainly on structure: which sparsity patterns preserve rank, how many faults can a cluster assignment tolerate, and where does the graph architecture create unavoidable vulnerability? These problems require tools from structural systems theory, graph theory, and combinatorics.

Our work studies structural rank, resilience of sparsity patterns, and fault-tolerant cluster assignment in multi-agent systems.

Structural rank and resilience of sparsity patterns
Structural-rank tools for certifying resilience of network sparsity patterns.

Representative Publications:

  1. M. Sharf and D. Zelazo, “Cluster assignment in multi-agent systems: Sparsity bounds and fault tolerance,” Asian Journal of Control, 27(1):63–75, 2025.
    Sharf2025a_J.pdf DOI: 10.1002/asjc.3149 Sharf2025a_J.bibtex
  2. 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 Belabbas2021a_J.bibtex

Related Publications:

  1. M. Sharf and D. Zelazo, “Cluster assignment in multi-agent systems: Sparsity bounds and fault tolerance,” Asian Journal of Control, 27(1):63–75, 2025.
    Sharf2025a_J.pdf DOI: 10.1002/asjc.3149 Sharf2025a_J.bibtex
  2. 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 Belabbas2021a_J.bibtex
  3. 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 Fabris2023_J.bibtex
  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 Fabris2022a_J.bibtex
  5. D. Mukherjee 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 Muhkerjee2017a_J.bibtex
  6. D. Mukherjee 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 Muhkerjee2017b_J.bibtex
  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, Mar. 2018.
    Mukherjee2016a.pdf Mukherjee2016a.slides Mukherjee2016a.bibtex
  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 Zelazo2014a_J.bibtex
  9. 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 Mukherjee2016b.bibtex