Q-learning Enabled Intelligent Energy Attack in Sustainable Wireless Communication Networks
Q-learning Enabled Intelligent Energy Attack in Sustainable Wireless Communication Networks
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DOI：
10.1109/icc42927.2021.9500772
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发表时间：
2021-06
期刊：
影响因子：
--
通讯作者：
Long Li;Yu Luo;Lina Pu
中科院分区：
文献类型：
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作者：
Long Li;Yu Luo;Lina Pu
在本文中，我们确定了一个新的安全问题，称为恶意能量攻击，在可持续无线通信网络（SWCN）。我们表明，通过提供额外的能量到特定的节点，恶意能源（MES）可以故意操纵SWCN的路由路径。能量攻击的效率取决于被攻击的节点。为了提高能量攻击的效率，强化学习技术，Q学习，被用来开发智能能量攻击（Q-IEA）的MES政策。通过与网络环境的交互，Q-IEA可以智能地采取攻击行动，而不必知道网络层路由方法的细节。该功能可以大大增强MES对不同路由协议和网络拓扑的适应性。仿真结果表明，Q-IEA可以显着操纵路由路径的目标业务的需求。
In this paper, we identify a new security issue, called the malicious energy attack, in sustainable wireless communication networks (SWCNs). We show that by providing extra energy to specific nodes, a malicious energy source (MES) can intentionally manipulate the routing path of SWCNs. The efficiency of energy attack depends on which nodes to be attacked. To enhance the efficiency of energy attack, a reinforcement learning technique, Q-Learning, is used to develop an intelligent energy attack (Q-IEA) policy for MES. Through interacting with the network environment, the Q-IEA can intelligently take attack actions without having to know the details of the routing method at the network layer. This function can greatly enhance the adaptability of MES to different routing protocols and network topologies. Simulation results verify that Q-IEA can significantly manipulate the routing path of the targeted traffic on demand.