Abstract
This paper examines the performance of a strategy for mapping the topology of a mobile ad hoc network (MANET), providing insight for network defenders to understand how much information an adversary could discern about a target network. Using this topology inference strategy, a network eavesdropper collects frame emission start- and end-times and uses these to detect the presence of link layer acknowledgements between devices and ultimately constructs a network topology. We show how the performance of this simple strategy varies as a function of the amount of data collected by the eavesdropper over time, the size of the target network, the speed of the nodes, and the nodes’ data generation rate. We derive analytical results that allow for the rapid computation of expected true positive rate and false positive rate for topology inference in a MANET; these are compared against simulation results. The analytical results are used to derive a sensible window of observation over which to perform inference, with guidance on when to discard stale data. The results are also used to recommend strategies for network defenders to frustrate the performance of an adversary’s network inference.
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Notes
- 1.
A non-link in this context is a pair of nodes for which there is no direct 1-hop connection. There are, on average, Lmax – L non-links in the network.
- 2.
In this work, we consider fixed time windows where all data in the window is treated equally until its age exceeds the window size. An alternative for future work would be to include a decay factor where data has less impact on an inference decision as it ages, as opposed to a strict step function like with our window.
- 3.
We note that the relationship between TPR and FPR for binary classification problems often involves plotting TPR versus FPR as a receiver operating characteristic (ROC) curve; however, for our purposes it is more informative to observe these values plotted against a common axis.
- 4.
Youden’s J statistic is typically expressed as J = sensitivity + specificity – 1. The sensitivity of a measurement in statistics is equal to the TPR, and the specificity is (1-FPR). These substitutions lead to J = TPR – FPR. The intuition behind the J statistic is that it is the point on the ROC curve furthest from chance.
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Brown, J.D., Salmanian, M., Willink, T.J. (2021). Analysis and Performance of Topology Inference in Mobile Ad Hoc Networks. In: Foschini, L., El Kamili, M. (eds) Ad Hoc Networks. ADHOCNETS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-030-67369-7_6
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