Due to their limited resources, Internet of Things (IoT) networks are vulnerable to attacks like aggressive denial-of-service (DoS) attacks aimed at draining device energy. IoT devices often have non-rechargeable or hard-to-recharge batteries, especially when deployed in hostile areas, making them prime targets for energy depletion attacks such as barrage attacks. To mitigate these threats, security systems must implement lightweight measures. This paper proposes a new game theory-based mechanism to defend IoT devices against energy depletion. Game theory effectively models the adversarial interactions between attackers and defenders. Our approach uses a dynamic game with incomplete information to derive optimal detection, defense, and attack strategies, establishing a Perfect Bayesian Nash Equilibrium (PBNE) to protect IoT device energy under constant attack. This dynamic game involves repeated interactions where at least one player lacks complete information about the other. The proposed model offers a high-performance solution for conserving IoT device energy. Simulation results demonstrate its effectiveness, showing that it can save, on average, 95.19% of the energy expended in receiving packets during an attack and can deter attackers. This approach ensures the sustainability of IoT networks against persistent energy depletion attacks.

Wireless Sensor Networks (WSNs) consist of devices communicating information wirelessly from a monitored field. Sensors are designed with limited energy resources pushing application designers to optimize energy consumption. It is common practice in WSNs to organize nodes in clusters with a device (designed as a cluster head) with superior energy resources. However, this clustered architecture exposes vulnerabilities, particularly to energy depletion attacks targeting the cluster head. Energy depletion attacks pose a significant threat to sensor node survival. To overcome such attacks, we propose a cyber deception defense mechanism based on game theory to model the actions between the attacker agent and the cluster head agent and hence, extract optimal strategies during conflicting interactions between the agents. In this paper, we propose using a game with incomplete information to find the Nash equilibrium point. Cyber deception, particularly the integration of a honeypot system, is employed to enhance the solution’s effectiveness in optimizing cluster head energy in the face of potential attacks. The proposed solution demonstrates its effectiveness in mitigating attacks of varying intensity against the cluster head.

Nowadays, with the evolution of technology, sensor networks have experienced a real boom. Due to their constitutions, sensors suffer from low security and are therefore susceptible to different types of attacks. Wireless sensor networks (WSNs) deployed in hostile environments suffer particularly from energetic attacks, i.e. attacks aimed at shortening the life cycle of sensors. Sensors have limited energy resources; replacing or recharging nodes in hostile environments is difficult. Attacks that cause a drain on the energy level are the most common attacks in a hostile environment and can lead to the death of sensors such as sleep denial attacks. In this paper, we design a game model using a signaling game within clusters that enables both detection and defense against attackers. In this paper, we identify and impose penalties on nodes that practice sleep deprivation torture in WSNs. The simulations showed that the model is able to force the attacker to behave normally in a WSN.

Practically, a botnet is spread over the Internet of Things (IoT) to ensure an attacker the control of a large number of devices. In this context, in which IoT users react to protect their devices against the threat, a zero-sum one-sided partially observable stochastic game (OS-POSG) model is proposed in which a defender strategically places honeypots in the IoT network in order to deceive attacker’s actions and mitigate the botnet propagation. No player (attacker and defender) observes the opponent’s action but, realistically, the attacker—who is the maximizer—has a perfect knowledge of the state of the network while the defender—who is the minimizer—only is informed of the decisions of IoT users. The objective is to find an optimal deception strategy for the defender that better limits from above the proportion of infected IoT devices. We show in numerous simulations the impact of the partial observation and of the strategic defender’s action on the particular metrics which are the maximum proportion of infected IoT devices during the botnet propagation and the time to botnet extinction in the IoT network.