Internet of Things (IoT) is a trending domain and has acquired much interest for various kinds of civilian applications. The purpose of IoT is to make objects accessible and interconnected via internet. Hence, security to IoT devices is a major issue because devices connected to the IoT network are resource-constrained. In IoT, the nodes exchange information using insecure internet, which makes the network exposed to different attacks. This article proposes a new intrusion detection strategy, namely, Taylor-spider monkey optimization-based deep belief network (Taylor-SMO-based DBN). The KDD features and the trust factors are employed for intrusion detection. The KDD features are subjected to the classification, which is progressed using a newly devised optimization algorithm, namely, Taylor-spider monkey optimization (Taylor-SMO)-based DBN. The proposed Taylor-SMO algorithm is designed by integrating the Taylor series and spider monkey optimization (SMO) algorithm and is employed to train the deep belief network (DBN) to achieve accurate intrusion detection. The proposed Taylor-SMO-based DBN outperformed other methods with maximal accuracy of 90%, false alarm rate of10%, precision of 90%, and recall of 92%, respectively.

中文翻译：

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基于信任的特征，用于使用深度学习检测物联网网络中的入侵者

物联网 (IoT) 是一个趋势领域，已经引起了各种民用应用的极大兴趣。物联网的目的是使对象可以通过互联网访问和互连。因此，物联网设备的安全性是一个主要问题，因为连接到物联网网络的设备是资源受限的。在物联网中，节点使用不安全的互联网交换信息，这使得网络面临不同的攻击。本文提出了一种新的入侵检测策略，即基于泰勒蜘蛛猴优化的深度信念网络（Taylor-SMO-based DBN）。KDD 特征和信任因子用于入侵检测。对 KDD 特征进行分类，使用新设计的优化算法进行分类，即基于泰勒蜘蛛猴优化 (Taylor-SMO) 的 DBN。提出的 Taylor-SMO 算法是通过整合泰勒级数和蜘蛛猴优化 (SMO) 算法设计的，并用于训练深度信念网络 (DBN) 以实现准确的入侵检测。提出的基于 Taylor-SMO 的 DBN 优于其他方法，最大准确率为 90%，误报率为 10%，准确率为 90%，召回率为 92%。