The increasing deployment of low-cost industrial IoT (IIoT) sensor platforms on industrial assets enables great opportunities for anomaly classification in industrial plants. The performance of such a classification model depends highly on the available training data. Models perform well when the training data comes from the same machine. However, as soon as the machine is changed, repaired, or put into operation in a different environment, the prediction often fails. For this reason, we investigate whether it is feasible to have a robust and transferable method for AI based anomaly classification using different models and pre-processing steps on centrifugal pumps which are dismantled and put back into operation in the same as well as in different environments. Further, we investigate the model performance on different pumps from the same type compared to those from the training data.
Center for Digital Safety and Security Austrian Institute of Technology（奥地利科技学院数字安全与保障中心）；Distributed AI Systems Research Group Siemens Technology Austria（西门子奥地利技术公司分布式人工智能系统研究小组）
The increasing deployment of low-cost IoT sensor platforms in industry boosts the demand for anomaly detection solutions that fulfill two key requirements: minimal configuration effort and easy transferability across equipment. Recent advances in deep learning, especially long-short-term memory (LSTM) and autoencoders, offer promising methods for detecting anomalies in sensor data recordings. We compared autoencoders with various architectures such as deep neural networks (DNN), LSTMs and convolutional neural networks (CNN) using a simple benchmark dataset, which we generated by operating a pe…
Vellore Institute of Technology（韦洛尔理工大学）；King Saud University（沙特国王大学）
Generally, the risks associated with malicious threats are increasing for the IIoT and its related applications due to dependency on the Internet and the minimal resource availability of IoT devices. Thus, anomaly-based intrusion detection models for IoT networks are vital. Distinct detection methodologies need to be developed for the IIoT network as threat detection is a significant expectation of stakeholders. Machine learning approaches are considered to be evolving techniques that learn with experience, and such approaches have resulted in superior performance in various applications, such…
Federated Learning framework for LoRaWAN-enabled IIoT communication: A case study
Centre of Informatics and Systems of the University of Coimbra, CISUC（科英布拉大学信息与系统中心）；University of Coimbra（科英布拉大学）；Institute for Systems Engineering and Computers, INESC（系统工程与计算机研究所）；Sul-Rio-Grandense Federal Institute (IFSUL)（南里奥格兰德联邦学院）；Instituto de Telecomunicações（电信研究所）；Polytechnic Institute of Coimbra（科英布拉理工学院）；Coimbra Business School（科英布拉商学院）；INESC Coimbra（科英布拉INESC）
Real-Time Adaptive Anomaly Detection in Industrial IoT Environments
CIISE, Concordia University, Montréal, QC, Canada（Concordia大学）；Ericsson Research, Lund, Sweden（爱立信研究）；Computer Science Programme, University of Western Cape, Capetown, South Africa（南非开普敦大学计算机科学项目）
The University of Queensland（昆士兰大学）；Griffith University（格里菲斯大学）
Monitoring and detecting abnormal events in cyber-physical systems is crucial to industrial production. With the prevalent deployment of the Industrial Internet of Things (IIoT), an enormous amount of time series data is collected to facilitate machine learning models for anomaly detection, and it is of the utmost importance to directly deploy the trained models on the IIoT devices. However, it is most challenging to deploy complex deep learning models such as Convolutional Neural Networks (CNNs) on these memory-constrained IIoT devices embedded with microcontrollers (MCUs). To alleviate the m…
Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Series
Department of Electrical and Electronic Engineering, Imperial College London（电子工程系，帝国理工学院伦敦分校）；Centre for Quantum Engineering, Science and Technology (QuEST), Imperial College London（量子工程、科学与技术中心（QuEST），帝国理工学院伦敦分校）；Graduate Institute of Applied Physics, National Taiwan University（应用物理研究所，国立台湾大学）
Seoul National University（首尔大学）；Samsung Electronics Co. Ltd.（三星电子株式会社）
In this paper, we propose Squeezed Convolutional Variational AutoEncoder (SCVAE) for anomaly detection in time series data for Edge Computing in Industrial Internet of Things (IIoT). The proposed model is applied to labeled time series data from UCI datasets for exact performance evaluation, and applied to real world data for indirect model performance comparison. In addition, by comparing the models before and after applying Fire Modules from SqueezeNet, we show that model size and inference times are reduced while similar levels of performance is maintained.
Heilongjiang University（黑龙江大学）；École de technologie supérieure, Université du Québec（魁北克大学高等技术学院）；Nanyang Technological University（南洋理工大学）；Wuhan University of Technology（武汉理工大学）；Alibaba-NTU Joint Research Institute（阿里巴巴-南洋理工大学联合研究院）
Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies is becoming increasingly important. Furthermore, data collected by the edge device may contain the user's private data, which is challenging the current detection approaches as user privacy is calling for the public concern in recent years. With this focus, this paper proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. Specifically,…
As the Industrial Internet of Things (IIoT) grows, systems are increasingly being monitored by arrays of sensors returning time-series data at ever-increasing 'volume, velocity and variety' (i.e. Industrial Big Data). An obvious use for these data is real-time systems condition monitoring and prognostic time to failure analysis (remaining useful life, RUL). (e.g. See white papers by Senseye.io, and output of the NASA Prognostics Center of Excellence (PCoE).) However, as noted by Agrawal and Choudhary 'Our ability to collect "big data" has greatly surpassed our capability to analyze it, undersc…
It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of machine learning in countering these susceptibilities. Fol…
Digital Twin-Driven Communication-Efficient Federated Anomaly Detection for Industrial IoT
Department of Electronic Systems, Norwegian University of Science and Technology（电子系统系，挪威科学与技术大学）；Department of Engineering Cybernetics, Norwegian University of Science and Technology（工程控制系，挪威科学与技术大学）；Department of Gas Technology, SINTEF Energy Research（气体技术系，SINTEF能源研究）
Reliability-Calibrated Edge-IoT Early Fault Warning for Rotating Machinery with a Physics-Guided Tiny-Mamba Transformer
Great Bay University（大亚湾大学）；Huizhou University（惠州大学）；National University of Singapore（国立新加坡大学）；Shenzhen University（深圳大学）；James Cook University（詹姆斯库克大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Early fault detection (EFD) of rolling bearings can recognize slight deviation of the health states and contribute to the stability of mechanical systems. In practice, very limited target bearing data are available to conduct EFD, which makes it hard to adapt to the EFD task of new bearings. To address this problem, many transfer learning based EFD methods utilize historical data to learn transferable domain knowledge and conduct early fault detection on new target bearings. However, most existing methods only consider the distribution drift across different working conditions but ignore the d…
CISPA Helmholtz Center for Information Security（CISPA 信息安全亥姆霍兹中心）；Saarland University（萨尔兰大学）；Delft University of Technology（代尔夫特理工大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Politecnico di Milano（米兰理工大学）
Recently, reconstruction-based anomaly detection was proposed as an effective technique to detect attacks in dynamic industrial control networks. Unlike classical network anomaly detectors that observe the network traffic, reconstruction-based detectors operate on the measured sensor data, leveraging physical process models learned a priori. In this work, we investigate different approaches to evade prior-work reconstruction-based anomaly detectors by manipulating sensor data so that the attack is concealed. We find that replay attacks (commonly assumed to be very strong) show bad performance …
