In this paper, we apply a fusion machine learning method to construct an automatic intrusion detection system. Concretely, we employ the orthogonal variance decomposition technique to identify the relevant features in network traffic data. The selected features are used to build a deep neural network for intrusion detection. The proposed algorithm achieves 100% detection accuracy in identifying DDoS attacks. The test results indicate a great potential of the proposed method.

翻译：在本文中,我们使用聚变机学习方法来建立自动入侵探测系统。具体地说,我们使用正向差异分解技术来识别网络流量数据的相关特征。选定特征用来构建入侵探测的深神经网络。提议的算法在识别DDoS攻击时达到100%的检测准确度。测试结果表明,拟议方法有很大潜力。