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1. Learning Trust Over Directed Graphs in Multiagent Systems

   Akgün, O.E.; Dayı, A.K.; Gil, S.; Nedíc, A. (January 2023, 5th Annual Conference on Learning for Dynamics and Control)

   We address the problem of learning the legitimacy of other agents in a multiagent network when an unknown subset is comprised of malicious actors. We specifically derive results for the case of directed graphs and where stochastic side information, or observations of trust, is available. We refer to this as “learning trust” since agents must identify which neighbors in the network are reliable, and we derive a protocol to achieve this. We also provide analytical results showing that under this protocol i) agents can learn the legitimacy of all other agents almost surely, and that ii) the opinions of the agents converge in mean to the true legitimacy of all other agents in the network. Lastly, we provide numerical studies showing that our convergence results hold in practice for various network topologies and variations in the number of malicious agents in the network. Keywords: Multiagent systems, adversarial learning, directed graphs, networked systems 

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2. Exploiting Trust for Resilient Hypothesis Testing withMalicious Robots

   https://doi.org/10.1109/TRO.2024.3415235  

   Cavorsi, Matthew; Akgün, Orhan Eren; Yemini, Michal; Goldsmith, Andrea J; Gil, Stephanie (January 2024, IEEE Transactions on Robotics)

   We develop a resilient binary hypothesis testing framework for decision making in adversarial multi-robot crowdsensing tasks. This framework exploits stochastic trust observations between robots to arrive at tractable, resilient decision making at a centralized Fusion Center (FC) even when i) there exist malicious robots in the network and their number may be larger than the number of legitimate robots, and ii) the FC uses one-shot noisy measurements from all robots. We derive two algorithms to achieve this. The first is the Two Stage Approach (2SA) that estimates the legitimacy of robots based on received trust observations, and provably minimizes the probability of detection error in the worst-case malicious attack. For the Two Stage Approach, we assume that the proportion of malicious robots is known but arbitrary. For the case of an unknown proportion of malicious robots, we develop the Adversarial Generalized Likelihood Ratio Test (A-GLRT) that uses both the reported robot measurements and trust observations to simultaneously estimate the trustworthiness of robots, their reporting strategy, and the correct hypothesis. We exploit particular structures in the problem to show that this approach remains computationally tractable even with unknown problem parameters. We deploy both algorithms in a hardware experiment where a group of robots conducts crowdsensing of traffic conditions subject to a Sybil attack on a mock-up road network. We extract the trust observations for each robot from communication signals which provide statistical information on the uniqueness of the sender. We show that even when the malicious robots are in the majority, the FC can reduce the probability of detection error to 30.5% and 29% for the 2SA and the A-GLRT algorithms respectively. 

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3. Exploiting Trust for Resilient Hypothesis Testing with Malicious Robots

   Cavorsi, M.; Akgün, O.E.; Yemini, M.; Goldsmith, A.; Gil, S. (September 2022, arXiv:2209.12285v1)

   We develop a resilient binary hypothesis testing framework for decision making in adversarial multi-robot crowdsensing tasks. This framework exploits stochastic trust observations between robots to arrive at tractable, resilient decision making at a centralized Fusion Center (FC) even when i) there exist malicious robots in the network and their number may be larger than the number of legitimate robots, and ii) the FC uses one-shot noisy measurements from all robots. We derive two algorithms to achieve this. The first is the Two Stage Approach (2SA) that estimates the legitimacy of robots based on received trust observations, and provably minimizes the probability of detection error in the worst-case malicious attack. Here, the proportion of malicious robots is known but arbitrary. For the case of an unknown proportion of malicious robots, we develop the Adversarial Generalized Likelihood Ratio Test (A-GLRT) that uses both the reported robot measurements and trust observations to estimate the trustworthiness of robots, their reporting strategy, and the correct hypothesis simultaneously. We exploit special problem structure to show that this approach remains computationally tractable despite several unknown problem parameters. We deploy both algorithms in a hardware experiment where a group of robots conducts crowdsensing of traffic conditions on a mock-up road network similar in spirit to Google Maps, subject to a Sybil attack. We extract the trust observations for each robot from actual communication signals which provide statistical information on the uniqueness of the sender. We show that even when the malicious robots are in the majority, the FC can reduce the probability of detection error to 30.5% and 29% for the 2SA and the A-GLRT respectively. 

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4. Exploiting Trust for Resilient Hypothesis Testing with Malicious Robots

   https://doi.org/10.1109/ICRA48891.2023.10160385  

   Cavorsi, Matthew; Akgün, Orhan Eren; Yemini, Michal; Goldsmith, Andrea J.; Gil, Stephanie (May 2023, 2023 IEEE International Conference on Robotics and Automation (ICRA))

   We develop a resilient binary hypothesis testing framework for decision making in adversarial multi-robot crowdsensing tasks. This framework exploits stochastic trust observations between robots to arrive at tractable, resilient decisionmaking at a centralized Fusion Center (FC) even when i) there exist malicious robots in the network and their number may be larger than the number of legitimate robots, and ii) the FC uses one-shot noisy measurements from all robots.We derive two algorithms to achieve this. The first is the Two Stage Approach (2SA) that estimates the legitimacy of robots based on received trust observations, and provably minimizes the probability of detection error in the worst-case malicious attack. Here, the proportion of malicious robots is known but arbitrary. For the case of an unknown proportion of malicious robots, we develop the Adversarial Generalized Likelihood Ratio Test (A-GLRT) that uses both the reported robot measurements and trust observations to estimate the trustworthiness of robots, their reporting strategy, and the correct hypothesis simultaneously. We exploit special problem structure to show that this approach remains computationally tractable despite several unknown problem parameters.We deploy both algorithms in a hardware experiment where a group of robots conducts crowdsensing of traffic conditions on a mock-up road network similar in spirit toGoogleMaps, subject to a Sybil attack.We extract the trust observations for each robot from actual communication signals which provide statistical information on the uniqueness of the sender.We show that even when the malicious robots are in the majority, the FC can reduce the probability of detection error to 30.5% and 29% for the 2SA and the A-GLRT respectively. 

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5. AccountNet: Accountable Data Propagation Using Verifiable Peer Shuffling

   https://doi.org/10.1109/ICDCS57875.2023.00050  

   Yoon, Man-Ki (July 2023, IEEE)

   Collecting evidence of data that software systems produce and consume can provide critical information for reconstructing erratic behavior, tracing the origin of faults, and thus holding the responsible party accountable for particular consequences. However, when data propagates across trust boundaries with conflicting interests, they can be tempted to make evidence unprovable in order to avoid potential liability. Hence, we present a data propagation protocol that makes such attempts either detectable or ineffective by having data transfers witnessed by other network participants that collectively act as the prover for the data propagation process. The protocol builds an unstructured peer-to-peer overlay and is designed to disincentivize collusion among a malicious coalition of nodes by enforcing network participants to constantly exchange their partial views on the network in a random yet verifiable manner. A data producer or consumer who does not faithfully follow the protocol ends up having fewer witnesses from their side, making the network resistant to collusion with high probability. We derive the conditions under which this property holds and demonstrate the practicality and cost of our approach through the implementation of a distributed application built on top of the proposed protocol. 

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