FALCON: Framework for Anomaly Detection in Industrial Control Systems

Sapkota, Subin; Mehdy, A K M Nuhil; Reese, Stephen; Mehrpouyan, Hoda

doi:10.3390/electronics9081192

Cited by 13 publications

(7 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors reviewed supervised and unsupervised machine learning algorithms in [ 32 , 33 ] and presented their findings. Like other systems and processes, there are some limitations of machine learning, some of which are highlighted in [ 34 , 35 ]. The application of machine learning in setting up test sites for electronic products and analysis is presented in [ 36 ].…”

Section: Related Workmentioning

confidence: 99%

A Novel System to Increase Yield of Manufacturing Test of an RF Transceiver through Application of Machine Learning

Siddiqui

Otero

Zubair

2023

Sensors

View full text Add to dashboard Cite

Electronic manufacturing and design companies maintain test sites for a range of products. These products are designed according to the end-user requirements. The end user requirement, then, determines which of the proof of design and manufacturing tests are needed. Test sites are designed to carry out two things, i.e., proof of design and manufacturing tests. The team responsible for designing test sites considers several parameters like deployment cost, test time, test coverage, etc. In this study, an automated test site using a supervised machine learning algorithm for testing an ultra-high frequency (UHF) transceiver is presented. The test site is designed in three steps. Firstly, an initial manual test site is designed. Secondly, the manual design is upgraded into a fully automated test site. And finally supervised machine learning is applied to the automated design to further enhance the capability. The manual test site setup is required to streamline the test sequence and validate the control and measurements taken from the test equipment and unit under test (UUT) performance. The manual test results showed a high test time, and some inconsistencies were observed when the test operator was required to change component values to tune the UUT. There was also a sudden increase in the UUT quantities and so, to cater for this, the test site is upgraded to an automated test site while the issue of inconsistencies is resolved through the application of machine learning. The automated test site significantly reduced test time per UUT. To support the test operator in selecting the correct component value the first time, a supervised machine learning algorithm is applied. The results show an overall improvement in terms of reduced test time, increased consistency, and improved quality through automation and machine learning.

show abstract

Section: Related Workmentioning

confidence: 99%

A Novel System to Increase Yield of Manufacturing Test of an RF Transceiver through Application of Machine Learning

Siddiqui

Otero

Zubair

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In [43], the authors review machine learning algorithms. Their finding includes highlighting the limitations of some algorithms.…”

Section: Machine Learning Algorithm Reviewmentioning

confidence: 99%

A Machine Learning Based Full Duplex System Supporting Multiple Sign Languages for the Deaf and Mute

et al. 2023

View full text Add to dashboard Cite

This manuscript presents a full duplex communication system for the Deaf and Mute (D-M) based on Machine Learning (ML). These individuals, who generally communicate through sign language, are an integral part of our society, and their contribution is vital. They face communication difficulties mainly because others, who generally do not know sign language, are unable to communicate with them. The work presents a solution to this problem through a system enabling the non-deaf and mute (ND-M) to communicate with the D-M individuals without the need to learn sign language. The system is low-cost, reliable, easy to use, and based on a commercial-off-the-shelf (COTS) Leap Motion Device (LMD). The hand gesture data of D-M individuals is acquired using an LMD device and processed using a Convolutional Neural Network (CNN) algorithm. A supervised ML algorithm completes the processing and converts the hand gesture data into speech. A new dataset for the ML-based algorithm is created and presented in this manuscript. This dataset includes three sign language datasets, i.e., American Sign Language (ASL), Pakistani Sign Language (PSL), and Spanish Sign Language (SSL). The proposed system automatically detects the sign language and converts it into an audio message for the ND-M. Similarities between the three sign languages are also explored, and further research can be carried out in order to help create more datasets, which can be a combination of multiple sign languages. The ND-M can communicate by recording their speech, which is then converted into text and hand gesture images. The system can be upgraded in the future to support more sign language datasets. The system also provides a training mode that can help D-M individuals improve their hand gestures and also understand how accurately the system is detecting these gestures. The proposed system has been validated through a series of experiments resulting in hand gesture detection accuracy exceeding 95%.

show abstract

“…The authors highlighted limitations in the performance of machine-learning techniques in [22]. The use of historical data for detecting deviation from normal performance is mentioned in [23] along with the limitations of existing machine-learning algorithms and tools. The machine-learning algorithms are reviewed in [24] with the focus on quality control and how production lines can be analyzed and benefited from machine-learning techniques.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

A Universal Machine-Learning-Based Automated Testing System for Consumer Electronic Products

2021

View full text Add to dashboard Cite

Consumer electronic manufacturing (CEM) companies face a constant challenge to maintain quality standards during frequent product launches. A manufacturing test verifies product functionality and identifies manufacturing defects. Failure to complete testing can even result in product recalls. In this research, a universal automated testing system has been proposed for CEM companies to streamline their test process in reduced test cost and time. A universal hardware interface is designed for connecting commercial off-the-shelf (COTS) test equipment and unit under test (UUT). A software application, based on machine learning, is developed in LabVIEW. The test site data for around 100 test sites have been collected. The application automatically selects COTS test equipment drivers and interfaces on UUT and test measurements for test sites through a universal hardware interface. Further, it collects real-time test measurement data, performs analysis, generates reports and key performance indicators (KPIs), and provides recommendations using machine learning. It also maintains a database for historical data to improve manufacturing processes. The proposed system can be deployed standalone as well as a replacement for the test department module of enterprise resource planning (ERP) systems providing direct access to test site hardware. Finally, the system is validated through an experimental setup in a CEM company.

show abstract

FALCON: Framework for Anomaly Detection in Industrial Control Systems

Cited by 13 publications

References 26 publications

A Novel System to Increase Yield of Manufacturing Test of an RF Transceiver through Application of Machine Learning

A Novel System to Increase Yield of Manufacturing Test of an RF Transceiver through Application of Machine Learning

A Machine Learning Based Full Duplex System Supporting Multiple Sign Languages for the Deaf and Mute

A Universal Machine-Learning-Based Automated Testing System for Consumer Electronic Products

Contact Info

Product

Resources

About