With the increasing population of Industry 4.0, industrial big data (IBD) has become a hotly discussed topic in digital and intelligent industry field. The security problem existing in the signal processing on large scale of data stream is still a challenge issue in industrial internet of things, especially when dealing with the high-dimensional anomaly detection for intelligent industrial application. In this article, to mitigate the inconsistency between dimensionality reduction and feature retention in imbalanced IBD, we propose a variational long short-term memory (VLSTM) learning model for intelligent anomaly detection based on reconstructed feature representation. An encoder-decoder neural network associated with a variational reparameterization scheme is designed to learn the low-dimensional feature representation from high-dimensional raw data. Three loss functions are defined and quantified to constrain the reconstructed hidden variable into a more explicit and meaningful form. A lightweight estimation network is then fed with the refined feature representation to identify anomalies in IBD. Experiments using a public IBD dataset named UNSW-NB15 demonstrate that the proposed VLSTM model can efficiently cope with imbalance and highdimensional issues, and significantly improve the accuracy and reduce the false rate in anomaly detection for IBD according to F1, area under curve (AUC), and false alarm rate (FAR).

Along with the popularity of the Internet of Things (IoT) techniques with several computational paradigms, such as cloud and edge computing, microservice has been viewed as a promising architecture in largescale application design and deployment. Due to the limited computing ability of edge devices in distributed IoT, only a small scale of data can be used for model training. In addition, most of the machine-learning-based intrusion detection methods are insufficient when dealing with imbalanced dataset under limited computing resources. In this article, we propose an optimized intra/inter-class-structure-based variational few-shot learning (OICS-VFSL) model to overcome a specific out-of-distribution problem in imbalanced learning, and to improve the microservice-oriented intrusion detection in distributed IoT systems. Following a newly designed VFSL framework, an intra/inter-class optimization scheme is developed using reconstructed feature embeddings, in which the intra-class distance is optimized based on the approximation during a variation Bayesian process, while the inter-class distance is optimized based on the maximization of similarities during a feature concatenation process. An intelligent intrusion detection algorithm is, then, introduced to improve the multiclass classification via a nonlinear neural network. Evaluation experiments are conducted using two public datasets to demonstrate the effectiveness of our proposed model, especially in detecting novel attacks with extremely imbalanced data, compared with four baseline methods.

The impact of Internet of Things (IoT) has become increasingly significant in smart manufacturing, while Deep Generative Model (DGM) is viewed as a promising learning technique to work with large amount of continuously generated industrial big data in facilitating modern industrial applications. However, it is still challenging to handle the imbalanced data when using conventional Generative Adversarial Network (GAN) based learning strategies. In this study, we propose a Distribution Bias aware Collaborative Generative Adversarial Network (DB-CGAN) model for imbalanced deep learning in industrial IoT, especially to solve limitations caused by distribution bias issue between the generated data and original data, via a more robust data augmentation. An integrated data augmentation framework is constructed by introducing a complementary classifier into the basic GAN model. Specifically, a conditional generator with random labels is designed and trained adversarially with the classifier to effectively enhance augmentation of the number of data samples in minority classes, while a weight sharing scheme is newly designed between two separated feature extractors, enabling the collaborative adversarial training among generator, discriminator, and classifier. An augmentation algorithm is then developed for intelligent anomaly detection in imbalanced learning, which can significantly improve the classification accuracy based on the correction of distribution bias using the rebalanced data. Compared with five baseline methods, experiment evaluations based on two real-world imbalanced datasets demonstrate the outstanding performance of our proposed model in tackling the distribution bias issue for multi-class classification in imbalanced learning for industrial IoT applications.