A deep learning- based frechet and dirichlet model for intrusion detection in IWSN

Abstract

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.