Network security has become increasingly important in recent decades, while intrusion detection system plays a critical role in protecting it. Various machine learning techniques have been applied to intrusion detection, among which SVM has been considered as an effective method. However, existing studies rarely take the data quality into consideration, which is essential for constructing a well-performed intrusion detection system beyond machine learning techniques. In this paper, we propose an effective intrusion detection framework based on SVM with naïve Bayes feature embedding. Specifically, the naïve Bayes feature transformation technique is implemented on the original features to generate new data with high quality; then, an SVM classifier is trained using the transformed data to build the intrusion detection model. Experiments on multiple datasets in intrusion detection domain validate that the proposed detection method can achieve good and robust performances, with 93.75% accuracy on UNSW-NB15 dataset, 98.92% accuracy on CICIDS2017 dataset, 99.35% accuracy on NSL-KDD dataset and 98.58% accuracy on Kyoto 2006+ dataset. Furthermore, our method possesses huge advantages in terms of accuracy, detection rate and false alarm rate when compared to other methods.

近几十年来，网络安全已变得越来越重要，而入侵检测系统在保护网络安全方面起着至关重要的作用。各种机器学习技术已经应用于入侵检测，其中SVM被认为是一种有效的方法。但是，现有研究很少考虑数据质量，这对于构建超越机器学习技术的性能良好的入侵检测系统至关重要。在本文中，我们提出了一种基于SVM和朴素贝叶斯特征嵌入的有效入侵检测框架。具体来说，朴素的贝叶斯特征转换技术是在原始特征上实现的，可以生成高质量的新数据。然后，使用转换后的数据训练SVM分类器，以构建入侵检测模型。在入侵检测领域的多个数据集上进行的实验验证了所提出的检测方法能够实现良好而强大的性能，其中UNSW-NB15数据集的准确度为93.75％，CICIDS2017数据集的准确度为98.92％，NSL-KDD数据集的准确度为99.35％，准确度为98.58％在Kyoto 2006+数据集上。此外，与其他方法相比，我们的方法在准确性，检测率和误报率方面具有巨大优势。