Sequential Pattern Artificial Neural Network (ANN) Method for Predicting Attacker Transaction in Wireless Sensor Network (WSN)

Nowadays, Wireless Sensor Network (WSN) have created tremendous prominence because this evident benefits while comparing to the conventional cable network are simple in distribution, users mobility over the area of network coverage and ease in new user’s connection. With sudden increase of communications in digital technologies are depressed through traffic of network data. The usage demand in an internet has grown daily use for the present cyber world have influences the requirement of network security. The WSN security can be supported through the strategies of attack and defense using Machine Learning (ML) which perform automated in learning from and adapt to the characteristics of wireless communication which are firm in capturing using the features and model of hand-crafted. However, several security attacks have classified as cyber ones and the attack types are interested in the system tentative as well as spoils the data instead of data stealing. Hence, the paper focuses on Intrusion Detection System (IDS) has done through Stacked Autoencoder (SAE) with Artificial Neural Network (ANN) in sequential patterns method is produced improved security in DoS attacks. Moreover, the dataset of security type for DoS in WSN has mainly focused in identifying attacks, malicious node elimination and transmission data security