A game-theoretic analysis of energy-depleting jamming attacks with a learning counterstrategy

Jamming may become a serious threat in Internet of Things networks of battery-powered nodes, as attackers can disrupt packet delivery and significantly reduce the lifetime of the nodes. In this work, we model an active defense scenario in which an energy-limited node uses power control to defend itself from a malicious attacker, whose energy constraints may not be known to the defender. The interaction between the two nodes is modeled as an asymmetric Bayesian game where the victim has incomplete information about the attacker. We show how to derive the optimal Bayesian strategies for both the defender and the attacker, which may then serve as guidelines to develop and gauge efficient heuristics that are less computationally expensive than the optimal strategies. For example, we propose a neural-network-based learning method that allows the node to effectively defend itself from the jamming with a significantly reduced computational load. The outcomes of the ideal strategies highlight the tradeoff between node lifetime and communication reliability and the importance of an intelligent defense from jamming attacks.