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A survey of industrial control system testbeds
Industrial Control System(ICS) testbed is the basis of ICS safety research. In order to build an ICS testbed, it is necessary to have a deep understanding of the current research status. This paper introduced the structure and composition of the typical ICS system. Then it simply analyzed the technical characteristics, system framework and advantages and disadvantages of four types of typical ICS testbeds. Finally, it summarized application scenarios of ICS testbed and pointed out some problems in testbed construction and the development direction of ICS testbed with the fusion of cyber and physical.
Journal Article
Power and Energy Systems III
Selected, peer reviewed papers from the 2013 3rd International Conference on Power and Energy Systems (ICPES 2013), November 23-24, 2013, Bangkok, Thailand.
eBook
Machine learning in industrial control system (ICS) security: current landscape, opportunities and challenges
The advent of Industry 4.0 has led to a rapid increase in cyber attacks on industrial systems and processes, particularly on Industrial Control Systems (ICS). These systems are increasingly becoming prime targets for cyber criminals and nation-states looking to extort large ransoms or cause disruptions due to their ability to cause devastating impact whenever they cease working or malfunction. Although myriads of cyber attack detection systems have been proposed and developed, these detection systems still face many challenges that are typically not found in traditional detection systems. Motivated by the need to better understand these challenges to improve current approaches, this paper aims to (1) understand the current vulnerability landscape in ICS, (2) survey current advancements of Machine Learning (ML) based methods with respect to the usage of ML base classifiers (3) provide insights to benefits and limitations of recent advancement with respect to two performance vectors; detection accuracy and attack variety. Based on our findings, we present key open challenges which will represent exciting research opportunities for the research community.
Journal Article
A Machine Learning Approach for Anomaly Detection in Industrial Control Systems Based on Measurement Data
Attack detection problems in industrial control systems (ICSs) are commonly known as a network traffic monitoring scheme for detecting abnormal activities. However, a network-based intrusion detection system can be deceived by attackers that imitate the system’s normal activity. In this work, we proposed a novel solution to this problem based on measurement data in the supervisory control and data acquisition (SCADA) system. The proposed approach is called measurement intrusion detection system (MIDS), which enables the system to detect any abnormal activity in the system even if the attacker tries to conceal it in the system’s control layer. A supervised machine learning model is generated to classify normal and abnormal activities in an ICS to evaluate the MIDS performance. A hardware-in-the-loop (HIL) testbed is developed to simulate the power generation units and exploit the attack dataset. In the proposed approach, we applied several machine learning models on the dataset, which show remarkable performances in detecting the dataset’s anomalies, especially stealthy attacks. The results show that the random forest is performing better than other classifier algorithms in detecting anomalies based on measured data in the testbed.
Journal Article
Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study
Computer aided diagnosis (
CAD
) of biomedical images assists physicians for a fast facilitated tissue characterization. A scheme based on combining fuzzy logic (
FL
) and deep learning (
DL
) for automatic semantic segmentation (
SS
) of tumors in breast ultrasound (
BUS
) images is proposed. The proposed scheme consists of two steps: the first is a FL based preprocessing, and the second is a Convolutional neural network (
CNN
) based SS. Eight well-known CNN based SS models have been utilized in the study. Studying the scheme was by a dataset of 400 cancerous BUS images and their corresponding 400 ground truth images. SS process has been applied in two modes:
batch
and
one by one
image processing. Three quantitative performance evaluation metrics have been utilized: global accuracy (
GA
), mean Jaccard Index (mean intersection over union (
IoU
)), and mean BF (Boundary F1) Score.
In the batch processing mode
: quantitative metrics’ average results over the eight utilized CNNs based SS models over the 400 cancerous BUS images were:
95.45
% GA instead of
86.08
% without applying fuzzy preprocessing step,
78.70
% mean IoU instead of
49.61
%, and
68.08
% mean BF score instead of
42.63
%. Moreover, the resulted segmented images could show tumors’ regions more accurate than with only CNN based SS.
While, in one by one image processing mode:
there has been no enhancement neither qualitatively nor quantitatively. So, only when a batch processing is needed, utilizing the proposed scheme may be helpful in enhancing automatic ss of tumors in BUS images. Otherwise applying the proposed approach on a one-by-one image mode will disrupt segmentation’s efficiency. The proposed batch processing scheme may be generalized for an enhanced CNN based SS of a targeted region of interest (
ROI
) in any batch of digital images. A modified small dataset is available:
https://www.kaggle.com/mohammedtgadallah/mt-small-dataset
(S1 Data).
Journal Article
Flexomagnetism and vertically graded Néel temperature of antiferromagnetic Cr2O3 thin films
Antiferromagnetic insulators are a prospective materials platform for magnonics, spin superfluidity, THz spintronics, and non-volatile data storage. A magnetomechanical coupling in antiferromagnets offers vast advantages in the control and manipulation of the primary order parameter yet remains largely unexplored. Here, we discover a new member in the family of flexoeffects in thin films of Cr
2
O
3
. We demonstrate that a gradient of mechanical strain can impact the magnetic phase transition resulting in the distribution of the Néel temperature along the thickness of a 50-nm-thick film. The inhomogeneous reduction of the antiferromagnetic order parameter induces a flexomagnetic coefficient of about 15
μ
B
nm
−2
. The antiferromagnetic ordering in the inhomogeneously strained films can persist up to 100 °C, rendering Cr
2
O
3
relevant for industrial electronics applications. Strain gradient in Cr
2
O
3
thin films enables fundamental research on magnetomechanics and thermodynamics of antiferromagnetic solitons, spin waves and artificial spin ice systems in magnetic materials with continuously graded parameters.
Flexomagnetism refers to the modification of the magnetic properties of a material due to inhomogeneous strain, and offers a promising pathway to the control and manipulation of magnetism. Here, Makushko et al. explore flexomagnetism in antiferromagnetic thin films of Cr
2
O
3
, demonstrating a gradient of the Néel temperature as a result of an inhomogeneous strain.
Journal Article
Exploration and Application of Cloud-Network Integration Control Architecture in Industry
Cloud-network integration(CNI) is an inevitable choice to enhance the supply capacity of industrial Internet. In traditional industrial control systems, there are problems such as insufficient depth of perception, insufficient connectivity and insufficient analytical predictability. Firstly, an industrial CNI control architecture is proposed in this paper. Then a software-defined PLC integrated development & runtime environment based on the cloud framework is developed and deployed on a private cloud platform. An efficient 5G private network is created to facilitate wireless omnidirectional connectivity of cloud PLC. The problems of complex industrial field circuits and difficult to unify protocols are solved through 5G intelligent terminal devices. Finally, the cloud control architecture and software platform are successfully applied to the remote centralized control of the tundish preparation system in a steel enterprise’s two steelmaking plants. The results shows that the innovative system architecture meet the requirements for high-performance access, transmission, computing and storage in industrial plants, which achieves the cloud control of entire process industrial automation production.
Journal Article