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Wireless Sensor Networks Intrusion Detection Based on SMOTE and the Random Forest Algorithm

机译：基于SMOTE和随机森林算法的无线传感器网络入侵检测。

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摘要

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.
机译：随着无线传感器网络在军事和环境监控中的广泛应用，安全问题变得越来越突出。由于缺乏物理防御设备，通过无线传感器网络交换的数据容易受到恶意攻击。因此，迫切需要相应的入侵检测方案来防御此类攻击。考虑到入侵数据集严重的类不平衡，本文提出了一种利用合成少数抽样算法（SMOTE）来平衡数据集，然后使用随机森林算法训练分类器进行入侵检测的方法。在基准入侵数据集上进行了仿真，随机森林算法的准确率达到了92.39％，高于其他比较算法。在对少数样本进行过采样后，将随机森林与SMOTE结合使用的准确性提高到92.57％。这表明所提算法为解决类不平衡问题，提高入侵检测性能提供了有效的解决方案。

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