Research on the Intrusion Detection Model of Underwater Sensor Networks

The characteristics of underwater nodes, the harshness of the underwater environment, and the openness of the deployment area make underwater sensor networks vulnerable to various network attacks; in order to defend against potential threats and attacks, intrusion detection, as an active defense tec...

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Bibliographic Details
Published in:Journal of sensors 2022-05, Vol.2022, p.1-17
Main Authors: Huang, Haijie, Liu, Na, Chen, Dandan, Yang, Qiuling, Huang, Xiangdang
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Assaults
Classification
Clusters
Confidentiality
Data encryption
Energy consumption
Environmental monitoring
Feature extraction
Game theory
Intrusion
Machine learning
Military technology
Networks
Neural networks
Nodes
Oversampling
Principal components analysis
Sensors
Support vector machines
Underwater detectors
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Summary:The characteristics of underwater nodes, the harshness of the underwater environment, and the openness of the deployment area make underwater sensor networks vulnerable to various network attacks; in order to defend against potential threats and attacks, intrusion detection, as an active defense technology, can detect different attacks before they occur; currently, the technology is less used in underwater sensor networks and has a low detection rate for some types of attacks and cannot effectively detect the problem of multiple attack types. To address this problem, this paper proposes an intrusion detection model for underwater sensor networks for multiple types of attacks. Firstly, cluster head nodes use neighborhood rough sets for feature extraction, and the reduced dimensional data is transmitted to sink nodes to reduce the node computation. Further, the synthetic minority oversampling technique (SMOTE) is used to balance the data set, increase the number of minority class samples, and improve the detection rate of minority class attacks. Finally, determine whether a node is trusted according to the trust value of the cluster head node, and train the classifier using the random forest algorithm to detect the type of attack; it suffers to achieve intrusion detection of multiple types of attacks. Simulation results show that the model can not only improve the performance of intrusion detection of multitype attacks but also achieve an accuracy of over 99% for the detection of imbalance classes.
ISSN:1687-725X
1687-7268
DOI:10.1155/2022/2323747