Research papers in game-theoretic approaches for attack detection in the RPL (Routing Protocol for Low-Power and Lossy Networks) investigate how strategic decision-making models can be used to enhance the security of IoT environments. Since RPL is vulnerable to various attacks such as sinkhole, blackhole, selective forwarding, and rank manipulation, game theory provides a mathematical framework to model the interactions between attackers and defenders as rational players with conflicting objectives. In this context, attackers aim to maximize network disruption while minimizing their chances of being detected, whereas defenders strive to detect and mitigate malicious behavior with minimal energy and communication overhead. Different game-theoretic models—such as non-cooperative games, Stackelberg games, evolutionary games, and repeated games—have been applied to analyze the trade-offs between detection accuracy, resource consumption, and attack resilience. Some studies introduce reputation-based or trust-based payoffs, where honest forwarding is rewarded while misbehavior results in penalties, encouraging cooperation among nodes. Others combine game theory with intrusion detection systems (IDS), reinforcement learning, and optimization strategies to create adaptive defenses capable of responding to evolving attack strategies. Game-theoretic mechanisms are particularly suitable for RPL-based IoT networks because they allow decentralized and lightweight decision-making, which is critical for resource-constrained devices. However, challenges remain in designing scalable and realistic models that capture the unpredictability of adversarial behavior, reduce computational complexity, and ensure fairness among participating nodes. Overall, the literature highlights that game-theoretic approaches provide a powerful and systematic foundation for developing intelligent, adaptive, and energy-efficient attack detection mechanisms in RPL-based IoT networks.