Intrusion Detection Algorithm and Simulation of Wireless Sensor Network under Internet Environment

• Published in: Journal of sensors 2021-11, Vol.2021 (1)
• Main Author: Jin, Jing
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Classification; Classifiers; Datasets; False alarms; Feature extraction; Feature selection; Genetic algorithms; Internet access; Intrusion detection systems; Machine learning; Methods; Modules; R&D; Research & development; Sensors; Simulation; Software; Support vector machines; Training; Wireless networks; Wireless sensor networks
• ISSN: 1687-725X; 1687-7268
• DOI: 10.1155/2021/9089370

As an effective security protection technology, intrusion detection technology has been widely used in traditional wireless sensor network environments. With the rapid development of wireless sensor network technology and wireless sensor network applications, the wireless sensor network data traffic also grows rapidly, and various kinds of viruses and attacks appear. Based on the temporal correlation characteristics of the intrusion detection dataset, we propose a multicorrelation-based intrusion detection model for long- and short-term memory wireless sensor networks. The model selects the optimal feature subset through the information gain feature selection module, converts the feature subset into a TAM matrix using the multicorrelation analysis algorithm, and inputs the TAM matrix into the long- and short-term memory wireless sensor network module for training and testing. Aiming at the problems of low detection accuracy and high false alarm rate of traditional machine learning-based wireless sensor network intrusion detection models in the intrusion detection process, a wireless sensor network intrusion detection model combining two-way long- and short-term memory wireless sensor network and C5.0 classifier is proposed. The model first uses the hidden layer of the bidirectional long- and short-term memory wireless sensor network to extract the features of the intrusion detection data set and finally inputs extracted features into the C5.0 classifier for training and classification. In order to illustrate the applicability of the model, the experiment selects three different data sets as the experimental data sets and conducts simulation performance analysis through simulation experiments. Experimental results show that the model had better classification performance.