Reinforcement Learning Enabled Intelligent Energy Attack in Green IoT Networks
Long Li, Yu Luo, Jing Yang, Lina Pu
Abstract
In this paper, we study a new security issue brought by the renewable energy feature in green Internet of Things (IoT) network. We define a new attack method, called the malicious energy attack, where the attacker can charge specific nodes to manipulate routing paths. By intelligently selecting the victim nodes, the attacker can “encourage” most of the data traffic into passing through a compromised node and harm the information security. The performance of the energy attack depends on the charging strategies. We develop two reinforcement-learning enabled algorithms, namely, Q- learning enabled intelligent energy attack (Q-IEA) and Policy Gradient enabled intelligent energy attack (PG-IEA). Through interacting with the network environment, the attacker can intelligently take attack actions without knowing the private information of the IoT network. This can greatly enhance the adaptability of the attacker to different network settings. Simulation results verify that the proposed IEA methods can considerably increase the amount of traffic traveling through the compromised node. Compared with the network without attack, an additional 53.3% data traffic is lured to the compromised node, which is more than 4 times higher than the performance of Random Attack.