scholarly journals Machine Learning Methods for Anomaly Detection in IoT Networks, with Illustrations

2020 ◽  
pp. 287-295
Author(s):  
Vassia Bonandrini ◽  
Jean-François Bercher ◽  
Nawel Zangar
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Analysis of Machine Learning Methods in EtherCAT-Based Anomaly Detection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 184365-184374 ◽  
Author(s):  
Kevser Ovaz Akpinar ◽  
Ibrahim Ozcelik
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals A DOUBLE-SHRINK AUTOENCODER FOR NETWORK ANOMALY DETECTION

2020 ◽  
Vol 36 (2) ◽  
pp. 159-172
Author(s):  
Cong Thanh Bui ◽  
Loi Cao Van ◽  
Minh Hoang ◽  
Quang Uy Nguyen
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
State Of The Art ◽  
The State ◽  
The Internet ◽  
Learning Methods ◽  
Network Attacks ◽  
Machine Learning Methods ◽  
Pull Out ◽  
Network Anomaly Detection

The rapid development of the Internet and the wide spread of its applications has affected many aspects of our life. However, this development also makes the cyberspace more vulnerable to various attacks. Thus, detecting and preventing these attacks are crucial for the next development of the Internet and its services. Recently, machine learning methods have been widely adopted in detecting network attacks. Among many machine learning methods, AutoEncoders (AEs) are known as the state-of-the-art techniques for network anomaly detection. Although, AEs have been successfully applied to detect many types of attacks, it is often unable to detect some difficult attacks that attempt to mimic the normal network traffic. In order to handle this issue, we propose a new model based on AutoEncoder called Double-Shrink AutoEncoder (DSAE). DSAE put more shrinkage on the normal data in the middle hidden layer. This helps to pull out some anomalies that are very similar to normal data. DSAE are evaluated on six well-known network attacks datasets. The experimental results show that our model performs competitively to the state-of-the-art model, and often out-performs this model on the attacks group that is difficult for the previous methods.

scholarly journals Automatic Anomaly Detection on In-Production Manufacturing Machines Using Statistical Learning Methods

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2344 ◽  
Author(s):  
Federico Pittino ◽  
Michael Puggl ◽  
Thomas Moldaschl ◽  
Christina Hirschl
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Statistical Learning ◽  
Control Charts ◽  
Industry 4.0 ◽  
Production Environment ◽  
Statistical Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
And Control

Anomaly detection is becoming increasingly important to enhance reliability and resiliency in the Industry 4.0 framework. In this work, we investigate different methods for anomaly detection on in-production manufacturing machines taking into account their variability, both in operation and in wear conditions. We demonstrate how the nature of the available data, featuring any anomaly or not, is of importance for the algorithmic choice, discussing both statistical machine learning methods and control charts. We finally develop methods for automatic anomaly detection, which obtain a recall close to one on our data. Our developed methods are designed not to rely on a continuous recalibration and hand-tuning by the machine user, thereby allowing their deployment in an in-production environment robustly and efficiently.

scholarly journals Automatically Detecting Excavator Anomalies Based on Machine Learning

Symmetry ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 957 ◽  
Author(s):  
Qingqing Zhou ◽  
Guo Chen ◽  
Wenjun Jiang ◽  
Kenli Li ◽  
Keqin Li
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Expert Systems ◽  
Statistical Models ◽  
Working Condition ◽  
Research Work ◽  
Support Vector ◽  
Detection Accuracy ◽  
Learning Methods ◽  
Machine Learning Methods

Excavators are one of the most frequently used pieces of equipment in large-scale construction projects. They are closely related to the construction speed and total cost of the entire project. Therefore, it is very important to effectively monitor their operating status and detect abnormal conditions. Previous research work was mainly based on expert systems and traditional statistical models to detect excavator anomalies. However, these methods are not particularly suitable for modern sophisticated excavators. In this paper, we take the first step and explore the use of machine learning methods to automatically detect excavator anomalies by mining its working condition data collected from multiple sensors. The excavators we studied are from Sany Group, the largest construction machinery manufacturer in China. We have collected 40 days working condition data of 107 excavators from Sany. In addition, we worked with six excavator operators and engineers for more than a month to clean the original data and mark the anomalous samples. Based on the processed data, we have designed three anomaly detection schemes based on machine learning methods, using support vector machine (SVM), back propagation (BP) neural network and decision tree algorithms, respectively. Based on the real excavator data, we have carried out a comprehensive evaluation. The results show that the anomaly detection accuracy is as high as 99.88%, which is obviously superior to the previous methods based on expert systems and traditional statistical models.

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