Machine learning techniques are proven valuable for the Internet of things (IoT) due to intelligent and cost-effective computing processes. In recent decades, wireless sensor network (WSN) and machine learning are integrated to give significant improvements for energy-based systems. However, resourceful routes analytic with nominal energy consumption are some demanding challenges. Moreover, WSN operates in an unpredictable space and a lot of network threats can be harmful to smart and secure data gathering. Consequently, security against such threats is another major concern for low-power sensors. Therefore, we aim to present a machine learning-based approach for autonomous IoT Security to achieve optimal energy efficiency and reliable transmissions. First, the proposed protocol optimizes network performance using a model-free Q-learning algorithm and achieves fault-tolerant data transmission. Second, it accomplishes data confidentiality against adversaries using a cryptography-based deterministic algorithm. The proposed protocol demonstrates better conclusions than other existing solutions.

中文翻译：

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基于机器学习的自主物联网安全方法


由于智能且经济高效的计算过程，机器学习技术被证明对物联网 (IoT) 很有价值。近几十年来，无线传感器网络（WSN）和机器学习相结合，为基于能源的系统带来了显着的改进。然而，对名义能耗进行资源丰富的路线分析是一些艰巨的挑战。此外，无线传感器网络在不可预测的空间中运行，许多网络威胁可能会损害智能和安全的数据收集。因此，针对此类威胁的安全性是低功耗传感器的另一个主要问题。因此，我们的目标是提出一种基于机器学习的自主物联网安全方法，以实现最佳的能源效率和可靠的传输。首先，所提出的协议使用无模型Q学习算法优化网络性能并实现容错数据传输。其次，它使用基于密码学的确定性算法来实现针对对手的数据机密性。所提出的协议展示了比其他现有解决方案更好的结论。