College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.
School of Artificial Intelligence, Zhejiang Post and Telecommunication College, Shaoxing 312366, China.
Sensors (Basel). 2020 Oct 8;20(19):5710. doi: 10.3390/s20195710.
Owing to the constraints of time and space complexity, network intrusion detection systems (NIDSs) based on support vector machines (SVMs) face the "curse of dimensionality" in a large-scale, high-dimensional feature space. This study proposes a joint training model that combines a stacked autoencoder (SAE) with an SVM and the kernel approximation technique. The training model uses the SAE to perform feature dimension reduction, uses random Fourier features to perform kernel approximation, and then random Fourier mapping is explicitly applied to the sub-sample to generate the random feature space, making it possible to apply a linear SVM to uniformly approximate to the Gaussian kernel SVM. Finally, the SAE performs joint training with the efficient linear SVM. We studied the effects of an SAE structure and a random Fourier feature on classification performance, and compared that performance with that of other training models, including some without kernel approximation. At the same time, we compare the accuracy of the proposed model with that of other models, which include basic machine learning models and the state-of-the-art models in other literatures. The experimental results demonstrate that the proposed model outperforms the previously proposed methods in terms of classification performance and also reduces the training time. Our model is feasible and works efficiently on large-scale datasets.
由于时间和空间复杂度的限制，基于支持向量机（SVM）的网络入侵检测系统（NIDS）在大规模、高维特征空间中面临“维度诅咒”。本研究提出了一种联合训练模型，该模型将堆叠自动编码器（SAE）与 SVM 和核逼近技术结合在一起。该训练模型使用 SAE 进行特征降维，使用随机傅里叶特征进行核逼近，然后将随机傅里叶映射显式应用于子样本，以生成随机特征空间，从而可以使用线性 SVM 对高斯核 SVM 进行均匀逼近。最后，SAE 与高效的线性 SVM 进行联合训练。我们研究了 SAE 结构和随机傅里叶特征对分类性能的影响，并将其与其他训练模型（包括一些没有核逼近的模型）的性能进行了比较。同时，我们将所提出模型的准确性与其他模型（包括基本机器学习模型和其他文献中的最新模型）进行了比较。实验结果表明，所提出的模型在分类性能方面优于先前提出的方法，同时还减少了训练时间。我们的模型在大规模数据集上是可行的，并且效率很高。
