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Enhancing the security of OLSR protocol using reinforcement learning

机译：使用加强学习提高OLSR协议的安全性

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Mobile ad-hoc networks are used in various institutions such as the military, hospitals, and various businesses. Due to their dynamic mobile structure-free and self-adaptive nature, they are ideal to be used in emergency situations where the resources available are limited. The wireless range of the devices in the MANET is narrow. In order to communicate with the desired device often times it is necessary to use intermediate devices between the source and the destination. Therefore, it is important to secure sensitive information sent through intermediate devices. OLSR is a widely used MANET routing protocol. Although OLSR protocol has excelled in performance and reliability, it is rather poor in security. In this context, we attempt to improve the security of OLSR protocol with the aid of Q-Learning by selecting trustworthy nodes to forward messages. Behavior of the nodes is used to determine the trust of the nodes.
机译：移动ad-hoc网络用于各种机构，例如军事，医院和各种业务。由于它们的动态移动结构和自适应性质，它们是理想的可用于可用资源的紧急情况。 MANET中设备的无线范围窄。为了经常与所需的设备通信，有必要在源和目的地之间使用中间设备。因此，重要的是要确保通过中间设备发送的敏感信息。 OLSR是一种广泛使用的MANET路由协议。虽然OLSR协议在性能和可靠性方面具有卓越，但安全性相当差。在这种情况下，我们通过选择值得信赖的节点来转发消息来借助Q学习来尝试提高OLSR协议的安全性。节点的行为用于确定节点的信任。

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