Variational Few-Shot Learning for Microservice-Oriented Intrusion Detection in Distributed Industrial IoT

Abstract

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.

A Data-Secured Intelligent IoT System for Agricultural Environment Monitoring

“…In 2013, Ryan proposed the early concept of edge computing [14] to address the problem of the rapidly growing number of mobile edge devices. In recent years, distributed IoT architecture based on edge computing has attracted the attention from many researchers [15][16][17]. Existing studies or edge computing platforms only consider a single edge cloud's vertical application in an IoT scenario, without considering multiple edge clouds in heterogeneous scenarios.…”

Machine learning techniques for CT imaging diagnosis of novel coronavirus pneumonia: a review

“…This model ensures better feature representation for semantic segmentation by combining low-level to high-level feature maps, showing better quantitative and qualitative results with the same or fewer network parameters [ 49 ]. In 2020, Alom et al applied the NABLA-N network to segment CT and X-ray images of patients with COVID-19, delineating the infected regions of the lungs [ 50 ].…”

“…Alom et al [ 50 ] proposed a novel multi-task deep learning-based method for fast and efficient identification of COVID-19 patients based on the NABLA-N network segmentation model. It adopts the Inception Residual Recurrent Convolutional Neural Network and Transfer Learning (TL).…”

“…The NABLA-N network proposed by Alom et al [ 50 ] was trained on only 300 samples in its initial version. The system output showed some false positives due to the lack of labeled samples for lung segmentation of CT images.…”

CroApp: A CNN-Based Resource Optimization Approach in Edge Computing Environment

“…Intelligent applications with real-time response requirements surge especially convolutional neural networks (CNN) based applications such as face recognition, Natural Language Processing (NLP), intrusion detection, online medical, anomaly detection, etc. [5][6][7][8][9]. Gradually, the increasing deployment of these unleashing applications on mobile devices becomes more and more impractical due to the tight limitation of resource [6,10,11].…”