The digitization of different components of industry and inter-connectivity among indigenous networks have increased the risk of network attacks. Designing an intrusion detection system to ensure security of the industrial ecosystem is difficult as network traffic encompasses various attack types, including new and evolving ones with minor changes. The data used to construct a predictive model for computer networks has a skewed class distribution and limited representation of attack types, which differ from real network traffic. These limitations result in dataset shift, negatively impacting the machine learning models' predictive abilities and reducing the detection rate against novel attacks. To address the challenges, we propose a novel deep neural network based Meta-Learning framework; INformation FUsion and Stacking Ensemble (INFUSE) for network intrusion detection. First, a hybrid feature space is created by integrating decision and feature spaces. Five different classifiers are utilized to generate a pool of decision spaces. The feature space is then enriched through a deep sparse autoencoder that learns the semantic relationships between attacks. Finally, the deep Meta-Learner acts as an ensemble combiner to analyze the hybrid feature space and make a final decision. Our evaluation on stringent benchmark datasets and comparison to existing techniques showed the effectiveness of INFUSE with an F-Score of 0.91, Accuracy of 91.6%, and Recall of 0.94 on the Test+ dataset, and an F-Score of 0.91, Accuracy of 85.6%, and Recall of 0.87 on the stringent Test-21 dataset. These promising results indicate the strong generalization capability and the potential to detect network attacks.

翻译：工业和本地网络不同组件的数字化及其互联性增加了网络攻击的风险。设计入侵检测系统以确保工业生态系统的安全性十分困难，因为网络流量包含各种攻击类型，包括那些变化微小且不断演化的新攻击。用于构建计算机网络预测模型的数据存在类别分布偏斜，且攻击类型的代表性有限，这与真实网络流量不同。这些局限性导致数据集偏移，对机器学习模型的预测能力产生负面影响，并降低了对新型攻击的检测率。为应对这些挑战，我们提出了一种新颖的基于深度神经网络的元学习框架——信息融合与堆叠集成（INFUSE），用于网络入侵检测。首先，通过整合决策空间和特征空间创建混合特征空间。利用五种不同的分类器生成决策空间池。然后，通过深度稀疏自编码器学习攻击之间的语义关系，以丰富特征空间。最后，深度元学习器作为集成组合器，分析混合特征空间并做出最终决策。我们在严格的基准数据集上的评估以及与现有技术的比较表明，INFUSE具有有效性：在Test+数据集上达到F分数0.91、准确率91.6%和召回率0.94；在严格的Test-21数据集上达到F分数0.91、准确率85.6%和召回率0.87。这些令人鼓舞的结果表明，该框架具备强大的泛化能力以及检测网络攻击的潜力。