# Game Theory Meets Wireless Sensor Networks Security Requirements and Threats Mitigation: A Survey
## 4 Game Theory Defense Strategies Against WSN Attacks
### Cooperative and Non-Cooperative Games
#### 4.4.2 Non-Cooperative Games  
The non-cooperative games have been proposed to describe and model the competition between the benevolent nodes and selfish/malicious nodes. In the following we list some of the WSN security problems, which can be modeled by non-cooperative games. * **Zero-sum Game**: In [76], a game-theoretic approach based on zero-sum game was presented seeking routing secured against external intruders. The authors have developed the utility function as a combination of the energy consumption, probability of malicious nodes, and probability of dropped packets between check nodes (one or more through the forward path) to maximize the probability of attack detection. Intrusion detection framework for smart phone systems is proposed in [41] at which two players play a non-cooperative constant-sum game with complete information to achieve NE that leads to a defense strategy for the security server. In this game, the defender wishes to enhance the security level but the attacker wants to deteriorate it. The model gathers data and checks if the security server monitors attack or not, then calculates the payoff per each case using node ID as a node identity. Furthermore, in [87], the authors propose an intrusion packet detection at which the classical zero-sum game is chosen as a natural model for the behavior of the attacker/defender strategies. The utility functions for both the attacker and defender take into consideration the probability of detection. In [53], a zero-sum game is proposed to mitigate the selective forwarding attack in which the infected nodes turn malicious and select some packet to drop based on a stochastic formulation. The model utility function is the difference between the defense energy budget supplied by the IDS for all participant nodes and the attack energy budget applied to turn those nodes into malicious. In [52], the authors formulate a zero-sum game aiming at maximizing the transmission capacity in underwater sensor network based on the mitigation of jammers. The utility function of this game is maximized to achieve the optimal SNR based on two observers sensor nodes against the effect of the disruption caused by the jammer. * **Nonzero-sum Game**: In [82], a nonzero-sum game is proposed to detect the malicious nodes and their effect (e.g., dropping packets and routing error message to misdirect the path) due to the DoS attack impact. Two defense strategies are developed. The first strategy is based on dynamic source routing that aims at maximizing the payoff. The second strategy focuses on reputation and cooperation between neighbors which also aims at maximizing the payoff and enhancing the defense strategy. However, the developed algorithms suffer from unnecessary added cost in the utility function of the defender, and this cost is mainly related to continuous defending of nodes even when no attack is encountered. In [81], the authors introduce a defense strategy based on a nonzero-sum game and achieve NE using a Markov decision process to predict the most vulnerable sensor node. In [54], the proposed defense strategy aims at detecting and correcting the malicious sensor nodes that drop packets based on a nonzero-sum game using periodic collusion-resistant punishment mechanism. The model stimulates the detected malicious node(s) to react benevolently. * **Stackelberg Game**: In [2], the attack-defense interactions are modeled using a Stackelberg game, and the NE condition is derived which is sufficient to ensure that the sensed data are truthful within a nominal error bound. The defense strategy gives an authority to the defender to be the decision maker. The model is based on a thresholding defense strategy against the attacks. The thresholding is correlated to the trust score of the data items observed by the sensor nodes to detect the effect of the followers (attacker). In [56], the authors propose a Stackelberg game for detecting the reactive jamming attack in wireless networks aiming at maximizing the signal-to-interference-noise ratio (SINR).