Wireless Sensor Networks Intrusion Detection Based on SMOTE and the Random Forest Algorithm
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...
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| Veröffentlicht in: | Sensors (Basel, Switzerland) Switzerland), 2019-01, Vol.19 (1), p.203 |
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| creator | Tan, Xiaopeng Su, Shaojing Huang, Zhiping Guo, Xiaojun Zuo, Zhen Sun, Xiaoyong Li, Longqing |
| description | 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. |
| doi_str_mv | 10.3390/s19010203 |
| format | Article |
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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%. 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| subjects | Algorithms Artificial intelligence Classification Clinical decision making Computer simulation Data mining Decision making Defense Embedded systems Environmental monitoring Information processing International conferences Intrusion detection systems Military equipment Neural networks Principal components analysis Random variables Remote sensors Sensors Software Wireless communications Wireless networks Wireless sensor networks |
| title | Wireless Sensor Networks Intrusion Detection Based on SMOTE and the Random Forest Algorithm |
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