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

Federico Chiariotti, Chiara Pielli, Nicola Laurenti, Andrea Zanella, Michele Zorzi

Research output: Contribution to journalJournal articleResearchpeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Article number6
JournalACM Transactions on Sensor Networks
Issue number1
Publication statusPublished - Dec 2019
Externally publishedYes

Bibliographical note

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© 2019 Copyright held by the owner/author(s).


  • Media access control
  • Multichannel
  • Radio interference
  • Time synchronization
  • Wireless sensor networks


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