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A multiplayer game model to detect insiders in wireless sensor networks.

Ioanna Kantzavelou¹, Leandros Maglaras², Panagiotis F Tzikopoulos³

¹University of West Attica, Athens, Greece.

Peerj. Computer Science

|February 17, 2022

Summary

A game theory model, GoWiSeN, addresses insider attacks in Wireless Sensor Networks (WSNs). It uses Local and Global Intrusion Detection Systems to identify and isolate compromised nodes, enhancing network security.

Keywords:

Game theory Intrusion detection Multiplayer game Wireless sensor networks

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Area of Science:

Computer Science
Network Security
Game Theory

Background:

Insider threats pose significant risks to Wireless Sensor Networks (WSNs) due to attackers' privileged access.
Compromised nodes can disrupt normal network operations, necessitating rapid detection and mitigation strategies.

Purpose of the Study:

To propose a novel game theory model, the Game of Wireless Sensor Networks (GoWiSeN), for detecting and mitigating insider attacks in WSNs.
To develop a framework that utilizes local and global intrusion detection systems to identify and isolate malicious nodes.

Main Methods:

Formulated an imperfect information, non-cooperative game theory model assuming rational players.
Integrated Local Intrusion Detection Systems (LIDSs) communicating with a Global Intrusion Detection System (GIDS).
Utilized extensive form game representation and von Neumann-Morgenstern utility functions to quantify outcomes and payoffs.

Main Results:

The GoWiSeN model was solved by locating Nash Equilibria (NE) in both pure and mixed strategies.
Experimental evaluations on real network datasets demonstrated the model's efficiency in detecting and isolating compromised nodes.
Simulations included various Intrusion Detection System (IDS) capabilities and specific insider attack scenarios.

Conclusions:

The proposed GoWiSeN model effectively addresses insider attacks in WSNs.
The game theory approach provides a robust framework for real-time threat detection and response in WSNs.
The findings offer practical insights into securing WSNs against sophisticated insider threats.