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Information Source Detection in the SIR Model: A Sample Path Based Approach
"... Abstract—This paper studies the problem of detecting the information source in a network in which the spread of information follows the popular SusceptibleInfectedRecovered (SIR) model. We assume all nodes in the network are in the susceptible state initially except the information source which is ..."
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Abstract—This paper studies the problem of detecting the information source in a network in which the spread of information follows the popular SusceptibleInfectedRecovered (SIR) model. We assume all nodes in the network are in the susceptible state initially except the information source which is in the infected state. Susceptible nodes may then be infected by infected nodes, and infected nodes may recover and will not be infected again after recovery. Given a snapshot of the network, from which we know all infected nodes but cannot distinguish susceptible nodes and recovered nodes, the problem is to find the information source based on the snapshot and the network topology. We develop a sample path based approach where the estimator of the information source is chosen to be the root node associated with the sample path that most likely leads to the observed snapshot. We prove for infinitetrees, the estimator is a node that minimizes the maximum distance to the infected nodes. A reverseinfection algorithm is proposed to find such an estimator in general graphs. We prove that for gregular trees such that gq> 1, where g is the node degree and q is the infection probability, the estimator is within a constant distance from the actual source with high probability, independent of the number of infected nodes and the time the snapshot is taken. Our simulation results show that for tree networks, the estimator produced by the reverseinfection algorithm is closer to the actual source than the one identified by the closeness centrality heuristic. I.
Subjective Logic with Uncertain Partial Observations
"... Abstract—Subjective logic (SL) is an effective tool to manage and update beliefs over a set of mutually exclusive assertions. The method to update subjective beliefs from direct observations of assertions is well understood. Recent work has incorporated the SL framework to derive the belief update e ..."
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Abstract—Subjective logic (SL) is an effective tool to manage and update beliefs over a set of mutually exclusive assertions. The method to update subjective beliefs from direct observations of assertions is well understood. Recent work has incorporated the SL framework to derive the belief update equations for partial observations where the measurements are only statistically related to the assertions. This work further expands the notion of SL to consider uncertainty in the underlying statistical relationship between measurements and assertions. In other words, new methods are derived for SL that incorporate uncertainty in the reported likelihood of the assertions. Simulations demonstrate the utility of the new likelihood uncertainty aware belief update methods. I.
A Robust Information Source Estimator with Sparse Observations
"... Abstract—In this paper, we consider the problem of locating the information source with sparse observations. We assume that a piece of information spreads in a network following a heterogeneous susceptibleinfectedrecovered (SIR) model and that a small subset of infected nodes are reported, from wh ..."
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Abstract—In this paper, we consider the problem of locating the information source with sparse observations. We assume that a piece of information spreads in a network following a heterogeneous susceptibleinfectedrecovered (SIR) model and that a small subset of infected nodes are reported, from which we need to find the source of the information. We adopt the sample path based estimator developed in [1], and prove that on infinite trees, the sample path based estimator is a Jordan infection center with respect to the set of observed infected nodes. In other words, the sample path based estimator minimizes the maximum distance to observed infected nodes. We further prove that the distance between the estimator and the actual source is upper bounded by a constant independent of the number of infected nodes with a high probability on infinite trees. Our simulations on tree networks and real world networks show that the sample path based estimator is closer to the actual source than several other algorithms. I.
Introducing The Spatial Qualification Problem and Its Qualitative Model
"... (eds.). pp. 361393. Frommberger, L. (2008). Learning to behave in space: a qualitative spatial Representation for robot navigation with reinforcement learning. In:International Journal on Artificial ..."
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(eds.). pp. 361393. Frommberger, L. (2008). Learning to behave in space: a qualitative spatial Representation for robot navigation with reinforcement learning. In:International Journal on Artificial
PROTECT in the Ports of Boston, New York and Beyond: Experiences in Deploying Stackelberg Security Games with Quantal Response
"... Abstract While three deployed applications of game theory for security have recently been reported at AAMAS [21], we as a community remain in the early stages of these deployments; there is a continuing need to understand the core principles for innovative security applications of game theory. Towa ..."
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Abstract While three deployed applications of game theory for security have recently been reported at AAMAS [21], we as a community remain in the early stages of these deployments; there is a continuing need to understand the core principles for innovative security applications of game theory. Towards that end, this chapter presents PROTECT, a gametheoretic system deployed by the United States Coast Guard (USCG) in the port of Boston for scheduling their patrols. USCG has termed the deployment of PROTECT in Boston a success, and efforts are underway to test it in the port of New York, with the potential for nationwide deployment. PROTECT is premised on an attackerdefender Stackelberg game model and offers five key innovations. First, this system is a departure from the assumption of perfect adversary rationality noted in previous work, relying instead on a quantal response (QR) model of the adversary’s behavior. To the best of our knowledge, this is the first realworld deployment of the QR model. Second, to improve PROTECT’s efficiency, we generate a compact representation of the defender’s strategy space, exploiting equivalence and dominance. Third, we show how to practically model a real maritime patrolling problem as a Stackelberg game. Fourth, our experimental results illustrate that PROTECT’s QR model more robustly handles realworld uncertainties than a perfect rationality model does. Finally, in evaluating PROTECT, this chapter provides realworld data: (i) comparison of humangenerated vs. PROTECT security schedules, and (ii) results from an Adversarial Perspective Team’s (human mock attackers) analysis.
Subrahmanian, and Sapino 2009). We review results of SCARE for activities by Iraniansponsored
"... In this paper we introduce SCARE the SpatioCultural Abductive Reasoning Engine which implements an algorithm that solves spatial abduction problems (Shakarian, ..."
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In this paper we introduce SCARE the SpatioCultural Abductive Reasoning Engine which implements an algorithm that solves spatial abduction problems (Shakarian,
Military Operational Art and
"... rha.lindelauf.01[at]nlda.nl An adversary looking to disrupt a power grid may look to target certain substations and sources of power generation to initiate a cascading failure that maximizes the number of customers without electricity. This is particularly an important concern when the enemy has t ..."
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rha.lindelauf.01[at]nlda.nl An adversary looking to disrupt a power grid may look to target certain substations and sources of power generation to initiate a cascading failure that maximizes the number of customers without electricity. This is particularly an important concern when the enemy has the capability to launch cyberattacks as practical concerns (i.e. avoiding disruption of service, presence of legacy systems, etc.) may hinder security. Hence, a defender can harden the security posture at certain power stations but may lack the time and resources to do this for the entire power grid. We model a power grid as a graph and introduce the cascading failure game in which both the defender and attacker choose a subset of power stations such as to minimize (maximize) the number of consumers having access to producers of power. We formalize problems for identifying both mixed and deterministic strategies for both players, prove complexity results under a variety of different scenarios, identify tractable cases, and develop algorithms for these problems. We also perform an experimental evaluation of the model and game on a realworld power grid network. Empirically, we noted that the game favors the attacker as he benefits more from increased resources than the defender. Further, the minimax defense produces roughly the same expected payoff as an easytocompute deterministic load based (DLB) defense when played against a minimax attack strategy. However, DLB performs more poorly than minimax defense when faced with the attacker’s best response to DLB. This is likely due to the presence of lowload yet highpayoff nodes, which we also found in our empirical analysis.