Abstract
With the wide application of wireless sensor networks in military and environmental monitoring, security issues have become increasingly prominent. Data exchanged over wireless sensor networks is vulnerable to malicious attacks due to the lack of physical defense equipment. Therefore, corresponding schemes of intrusion detection are urgently needed to defend against such attacks. Considering the serious class imbalance of the intrusion dataset, this paper proposes a method of using the synthetic minority oversampling technique (SMOTE) to balance the dataset and then uses the random forest algorithm to train the classifier for intrusion detection. The simulations are conducted on a benchmark intrusion dataset, and the accuracy of the random forest algorithm has reached 92.39%, which is higher than other comparison algorithms. After oversampling the minority samples, the accuracy of the random forest combined with the SMOTE has increased to 92.57%. This shows that the proposed algorithm provides an effective solution to solve the problem of class imbalance and improves the performance of intrusion detection.
Highlights
The wireless sensor network is a distributed intelligent network system
To improve the situation of class imbalance of the dataset, this paper proposed a classification method of random forest combined with the synthetic minority oversampling technique (SMOTE)
The intrusion detection for wireless sensor networks is an important subject in the field of the security of wireless sensor networks
Summary
The wireless sensor network is a distributed intelligent network system. It is composed of a large number of micro sensor nodes deployed in the detection area, which have the ability of wireless communication and computing. Due to the advantages of the negative selection algorithm (NSA) in the classification domain, Sun et al [20] proposed a WSN-NSA intrusion detection model based on the improved V-detector algorithm for wireless sensor networks (WSN). In order to overcome the limitation of rule-based system, data mining technology is used in intrusion detection systems for wireless sensor networks. A data mining algorithm called random forest is applied in intrusion detection for wireless sensor networks. Lee et al [29] proposed a hybrid approach for real-time network intrusion detection systems (NIDS) They adopt the random forest (RF) for feature selection. The random forest algorithm is used to train the new training set and the classifier is generated to realize the intrusion detection for wireless sensor networks.
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