The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Wireless Sensor Networks Intrusion Detection Based on SMOTE and the Random Forest Algorithm
Open accessAbstract
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.
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Cites
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Cited by
CNN-Based Network Intrusion Detection against Denial-of-Service Attacks
Kim, J., Kim, J., Kim, H., Shim, M., & Choi, E. (2020) · Electronics · doi:10.3390/electronics9060916 · Cited by 420
MQTTset, a New Dataset for Machine Learning Techniques on MQTT
Vaccari, I., Chiola, G., Aiello, M., Mongelli, M., & Cambiaso, E. (2020) · Sensors · doi:10.3390/s20226578 · Cited by 292
Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection
Injadat, M., Moubayed, A., Nassif, A. B., & Shami, A. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.3014929 · Cited by 282
TS-IDS: Traffic-aware self-supervised learning for IoT Network Intrusion Detection
Nguyen, H., & Kashef, R. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110966 · Cited by 94
Meng, L., Zhang, A., Chen, C., Wang, X., Jiang, X., Tao, L., Fan, J., Wu, X., Dai, C., Zhang, Y., et al. (2021) · Sensors · doi:10.3390/s21030799 · Cited by 45
Metadata
- Authors
- Tan, X., Su, S., Huang, Z., Guo, X., Zuo, Z., Sun, X., & Li, L.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-01-08
- Volume
- 19
- Issue
- 1
- Pages
- 203
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2910197492
- Cited by
- 185
- References
- 2
- FWCI
- 10.74
- Type
- journal article
- Language
- en
- Open access
- gold
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- Tan, X., Su, S., Huang, Z., Guo, X., Zuo, Z., Sun, X., & Li, L. (2019). Wireless Sensor Networks Intrusion Detection Based on SMOTE and the Random Forest Algorithm. Sensors. https://doi.org/10.3390/s19010203
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