PulSec: Secure Element based framework for sensors anomaly detection in Industry 4.0

Since the concept of industry 4.0 was proposed in 2011, the trend of industry 4.0 has been surging around the world. Intelligent factory is one of the main research points in the industry 4.0 era. In order to improve the intelligent level of the factory, the connection-and-cognition ability has to be established for the factory and its equipment. Connection builds data pipes among equipment and systems while cognition automatically turns the data into knowledge. In an intelligent factory, industrial robot plays a leading role. Hence, the aim of this paper is to synthetically study connection and cognition of industrial robots in intelligent factories. To be specific, open platform communications unified architecture (OPC UA) is applied to establish heterogeneous connection of industrial robots with factory management software. A long short-term memory (LSTM) joint auto encoder method is proposed to establish the unsupervised anomaly detection cognition ability for industrial robot process (e.g. grinding, welding and assembling). In summary, this study puts OPC UA and LSTM auto encoder technology together to study heterogeneous connection and process anomaly detection of industrial robots in intelligent factory. The experimental results showed that the proposed method successfully realized heterogeneous connection of an industrial robot and detected process anomaly from the robot built-in sensors’ data.

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Behavior Based Anomaly Detection Model in SCADA System

MATEC Web of Conferences ◽

10.1051/matecconf/201817301011 ◽

2018 ◽

Vol 173 ◽

pp. 01011 ◽

Cited By ~ 1

Author(s):

Xiaojun Zhou ◽

Zhen Xu ◽

Liming Wang ◽

Kai Chen ◽

Cong Chen ◽

...

Keyword(s):

Anomaly Detection ◽

Control Systems ◽

Supervisory Control ◽

Industry 4.0 ◽

Industrial Control ◽

Industrial Control Systems ◽

Detection Model ◽

Scada System ◽

Behavior Based ◽

And Control

With the arrival of Industry 4.0, more and more industrial control systems are connected with the outside world, which brings tremendous convenience to industrial production and control, and also introduces many potential security hazards. After a large number of attack cases analysis, we found that attacks in SCADA systems can be divided into internal attacks and external attacks. Both types of attacks are inevitable. Traditional firewalls, IDSs and IPSs are no longer suitable for industrial control systems. Therefore, we propose behavior-based anomaly detection and build three baselines of normal behaviors. Experiments show that using our proposed detection model, we can quickly detect a variety of attacks on SCADA (Supervisory Control And Data Acquisition) systems.

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

Sensors ◽

10.3390/s20082344 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2344 ◽

Cited By ~ 7

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.

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