Distributed Function Calculation via Linear Iterative Strategies in the Presence of Malicious Agents
By Shreyas Sundaram, Christoforos N. Hadjicostis. IEEE Transactions on Automatic Control, 2011
485 citations recorded by the source corpus.
Subfields: Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/909393d2a6f09f7c75ff92e619ae082e62e15a86
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In Computer Networks and Communications
- Xiaohua Ge
- Qing-Long Han
- Francesco Bullo
- Karl Henrik Johansson
- Florian Dörfler
- Fabio Pasqualetti
- Ling Shi
- Henrik Sandberg
Citations in the corpus
- Attack Detection and Identification in Cyber-Physical Systems (2013, background): “Finally, with respect to [20] and our earlier work [21], we consider continuous-time descriptor systems, and we include parameters constraints.”
- Physical Authentication of Control Systems: Designing Watermarked Control Inputs to Detect Counterfeit Sensor Outputs (2015, background): “In scenarios [6, 7, 8, 9, 10], the attacker can either arbitrarily perturb the system along certain directions without being detected by any filter or cannot induce any perturbation, without incurring detection.”
- Defending Distributed Systems Against Adversarial Attacks: Consensus, Consensusbased Learning, and Statistical Learning (2020, background): “Adversary-resilient distributed computing dates back to the Byzantine General Problem in computer science [9], and is getting more and more research attention in communication and control communities [6, 22].”
- A survey of distributed optimization (2019, background): “…distributed resilient consensus has been well studied (see, e.g., LeBlanc, Zhang, Kout-soukos, & Sundaram, 2013; Pasqualetti, Bicchi, & Bullo, 2012; Sundaram & Hadjicostis, 2011 ), distributed resilient optimization with adversarial agents is less studied and there are only a limited number of…”
- A systems and control perspective of CPS security (2019, background): “For example, I can be in the form of mutual information [166] or the difference of agents’ values and their consensus value, if the goal is to reach the consensus amongst the agents [143].”
- Secure State Estimation and Control of Cyber-Physical Systems: A Survey (2021, background): “On the other hand, it should be noted that stealthy attacks are correctable via suitable strategies if the systems are strongly observable [28], [80], and become detectable if only partial sensors are attacked [83] or some critical sensors are protected [4].”
- Revealing stealthy attacks in control systems (2012, background): “Several instances of this scenario have been considered in the context of control systems, see [6], [7], [8] and references therein.”
- Optimal Denial-of-Service Attack Scheduling With Energy Constraint (2015, background): “Researchers have investigated security issues in CPS from different perspectives recently [7], [8].”
- Resilient Distributed Parameter Estimation With Heterogeneous Data (2018, unknown): “, [11], [12], [14], [18], [19], [35]).”
- Resilient Asymptotic Consensus in Robust Networks (2013, background): “…the Approximate Byzantine Consensus problem, in which the normal nodes are required to achieve approximate agreement2 (i.e., they should converge to a relatively small convex set contained in the convex hull of their initial values) in the presence of F -total Byzantine faults in finite time.”
- On the Performance Analysis of Resilient Networked Control Systems Under Replay Attacks (2013, background): “In the context of multi-agent systems, the papers of [25], [28] determine conditions under which consensus multi-agent systems can detect misbehaving agents.”
- Control-Theoretic Methods for Cyberphysical Security: Geometric Principles for Optimal Cross-Layer Resilient Control Systems (2015, methodology): “Because there exists a linking of size two from any two attack nodes to the output vertices, every set of two attackers is detectable, and every set of one attacker is identifiable (see the section “Fundamental Attack Detection and Identification Limitations” and [34], [29], [28]).”
- Secure Estimation and Control for Cyber-Physical Systems Under Adversarial Attacks (2012, background): “of reaching consensus in the presence of malicious agents [14].”