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 Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science)

2018 ◽  
Vol 12 ◽  
pp. 117793221875929 ◽  
Author(s):  
Irene Sui Lan Zeng ◽  
Thomas Lumley
Keyword(s):  
Machine Learning ◽  
Statistical Learning ◽  
Information Science ◽  
Molecular System ◽  
Principal Component ◽  
Statistical Machine Learning ◽  
Learning Methods ◽  
Integrated Omics ◽  
Types Of Information ◽  
Integrated Information

Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.

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.

Technical research of detection algorithmically generated malicious domain names using machine learning methods

2020 ◽  
Vol 7 (1) ◽  
pp. 37-43
Author(s):  
Hieu Duc Ho ◽  
Huong Van Ho
Keyword(s):  
Machine Learning ◽  
Viterbi Algorithm ◽  
Command And Control ◽  
Network Infrastructure ◽  
Learning Methods ◽  
Generation Algorithm ◽  
Technical Research ◽  
Domain Names ◽  
Machine Learning Methods ◽  
And Control

 Abstract— In recent years, many malware use domain generation algorithm for generating a large of domains to maintain their Command and Control (C&C) network infrastructure. In this paper, we present an approach for detecting malicious domain names using machine learning methods. This approach is using Viterbi algorithm and dictionary for constructing feature of domain names. The approach is demonstrated using a range of legitimate domains and a number of malicious algorithmically generated domain names. The numerical results show the efficiency of this method.Tóm tắt— Trong những năm gần đây, nhiều phần mềm độc hại sử dụng thuật toán sinh tên miền tạo ra lượng lớn các tên miền để duy trì cơ sở hạ tầng mạng ra lệnh và điều khiển (C&C). Trong bài báo này, chúng tôi trình bày một cách tiếp cận để phát hiện tên miền độc hại bằng phương pháp học máy. Cách tiếp cận này sử dụng thuật toán Viterbi và tập từ điển để trích xuất các đặc trưng của tên miền. Cách tiếp cận được thể hiện bằng cách sử dụng một lượng lớn các tên miền hợp pháp và một lượng lớn tên miền độc hại được tạo ra bằng thuật toán sinh tên miền. Các kết quả thực nghiệm đã chỉ ra tính hiệu quả của phương pháp. 

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