Industrial Wireless Sensor Network (IWSN) includes numerous sensor nodes that collect data about target objects and transmit to sink nodes (SN). During data transmission among nodes, intrusion detection is carried to improve data security and privacy. Intrusion detection system (IDS) examines the network for intrusions based on user activities. Several works have been done in the field of intrusion detection and different measures are carried out to increase data security from the issues related to black hole, Sybil attack, Worm hole, identity replication attack and etc. In various existing approaches, secure data transmission is not achieved, therefore resulted in compromising the security and privacy of IWSNs. Accurate intrusion detection is still challenging task in terms of improving security and intrusion detection rate. In order to improve intrusion detection rate (IDR) with minimum time, generalized Frechet Hyperbolic Deep and Dirichlet Secured (FHD-DS) data communication model is introduced. At first, Frechet Hyperbolic Deep Traffic (FHDT) feature extraction method is designed to extract more relevant network activities and inherent traffic features. With the help of extracted features, anomalous or normal data is predicted. Followed by Statistical Dirichlet Anomaly-based Intrusion Detection model is applied to discover intrusion. Here, Dirichlet distribution is evaluated to attain secure data transmission and significantly detect intrusions in WSNs. Experimental evaluation is carried out with KDD cup 99 dataset on factors such as IDR, intrusion detection time (IDT) and data delivery rate (DDR). The observed results show that the generalized FHD-DS data communication method achieves higher IDR with minimum time.

中文翻译：

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基于深度学习的Frechet和Dirichlet模型用于IWSN中的入侵检测

工业无线传感器网络（IWSN）包括许多传感器节点，这些传感器节点收集有关目标对象的数据并传输到接收器节点（SN）。在节点之间的数据传输期间，进行入侵检测以提高数据安全性和隐私性。入侵检测系统（IDS）根据用户活动检查网络是否存在入侵。在入侵检测领域已经完成了多项工作，并采取了各种措施来提高数据安全性，涉及黑洞，Sybil攻击，Worm漏洞，身份复制攻击等问题。在各种现有方法中，安全的数据传输是未实现，因此导致破坏了IWSN的安全性和私密性。就提高安全性和入侵检测率而言，准确的入侵检测仍然是一项艰巨的任务。为了在最短的时间内提高入侵检测率（IDR），引入了广义的Frechet双曲深度和Dirichlet安全（FHD-DS）数据通信模型。首先，Frechet双曲线深度流量（FHDT）特征提取方法旨在提取更多相关的网络活动和固有流量特征。借助提取的特征，可以预测异常或正常数据。其次，采用基于统计狄里克雷异常的入侵检测模型来发现入侵。在这里，对Dirichlet分布进行评估以实现安全的数据传输并显着检测WSN中的入侵。使用KDD cup 99数据集对IDR，入侵检测时间（IDT）和数据传输速率（DDR）等因素进行了实验评估。