Optimal Scanning Bandwidth Strategy Incorporating Uncertainty about Adversary’s Characteristics





Scanning, Bandwidth, Detection, Bayesian game


In this paper, we investigate the problem of designing a spectrum scanning strategy to detect an intelligent Invader who wants to utilize spectrum undetected for his/her unapproved purposes. To deal with this problem we model the situation as two games, between a Scanner and an Invader, and solve them sequentially. The first game is formulated to design the optimal (in maxmin sense) scanning algorithm, while the second one allows one to find the optimal values of the parameters for the algorithm depending on the parameters of the network. These games provide solutions for two dilemmas that the rivals face. The Invader’s dilemma consists of the following: the more bandwidth the Invader attempts to use leads to a larger payoff if he is not detected, but at the same time also increases the probability of being detected and thus fined. Similarly, the Scanner faces a dilemma: the wider the bandwidth scanned, the higher the probability of detecting the Invader, but at the expense of increasing the cost of building the scanning system. The equilibrium strategies are found explicitly and reveal interesting properties. In particular, we have found a discontinuous dependence of the equilibrium strategies on the network parameters, fine and the type of the Invader’s award. This discontinuity of the fine means that the network provider has to take into account a human/social factor since some threshold values of fine could be very sensible for the Invader, while in other situations simply increasing the fine has a minimal deterrence impact. Also we show how incomplete information about the Invader’s technical characteristics and reward (e.g. motivated by using different type of application, say, video-streaming or downloading files) can be incorporated into the scanning strategy to increase its efficiency.


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How to Cite

A. Garnaev and W. Trappe, “Optimal Scanning Bandwidth Strategy Incorporating Uncertainty about Adversary’s Characteristics”, EAI Endorsed Trans Mob Com Appl, vol. 2, no. 5, p. e6, Dec. 2014.