An IoT Solution for Air Quality Monitoring and Hazard Identification for Smart City Development

Ogu, Reginald E.; Achumba, Ifeyinwa E.; Okoronkwo, Chinomso D.; Chukwudebe, G. A.; Chukwuchekwa, Nkwachukwu

doi:10.1109/nigercon54645.2022.9803129

Cited by 3 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This digital revolution has exposed once air-gapped OT infrastructures to a myriad of new attack surfaces and vectors [7][8][9]. With the advancement in the Industrial Internet of Things (IIoT), early identification and prevention of attacks that can lead to PCN disasters can be achieved by continuous monitoring using algorithm-based smart monitoring systems [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Detection of Man-in-the-Middle (MitM) Cyber-Attacks in Oil and Gas Process Control Networks Using Machine Learning Algorithms

Obonna,

Opara,

Mbaocha

et al. 2023

Future Internet

View full text Add to dashboard Cite

Recently, the process control network (PCN) of oil and gas installation has been subjected to amorphous cyber-attacks. Examples include the denial-of-service (DoS), distributed denial-of-service (DDoS), and man-in-the-middle (MitM) attacks, and this may have largely been caused by the integration of open network to operation technology (OT) as a result of low-cost network expansion. The connection of OT to the internet for firmware updates, third-party support, or the intervention of vendors has exposed the industry to attacks. The inability to detect these unpredictable cyber-attacks exposes the PCN, and a successful attack can lead to devastating effects. This paper reviews the different forms of cyber-attacks in PCN of oil and gas installations while proposing the use of machine learning algorithms to monitor data exchanges between the sensors, controllers, processes, and the final control elements on the network to detect anomalies in such data exchanges. Python 3.0 Libraries, Deep-Learning Toolkit, MATLAB, and Allen Bradley RSLogic 5000 PLC Emulator software were used in simulating the process control. The outcomes of the experiments show the reliability and functionality of the different machine learning algorithms in detecting these anomalies with significant precise attack detections identified using tree algorithms (bagged or coarse ) for man-in-the-middle (MitM) attacks while taking note of accuracy-computation complexity trade-offs.

show abstract

Section: Introductionmentioning

confidence: 99%

Detection of Man-in-the-Middle (MitM) Cyber-Attacks in Oil and Gas Process Control Networks Using Machine Learning Algorithms

Obonna,

Opara,

Mbaocha

et al. 2023

Future Internet

View full text Add to dashboard Cite

show abstract

An Experimentation on CoAP Multi Factor Authentication Mechanism with Reputation for Internet of Things Constrained Devices and Low Power Wide Area Network

Bezerra

Boing

Souza

et al. 2023

2023 International Conference on Information Networking (ICOIN)

View full text Add to dashboard Cite

Detection of Man-in-the-Middle (MitM) Cyber-Attacks in Oil and Gas Process Control Networks using Machine Learning Algorithms

Obonna,

Opara,

Mbaocha

et al. 2023

Preprint

View full text Add to dashboard Cite

In recent times, the process control network (PCN) of oil and gas installation has been subjected to amorphous cyber-attacks which include Denial-of-Service (DoS), Distributed-Denial-of-Service (DDoS), Man-in-the-Middle (MitM) attacks, and this may have been caused majorly by the integration of open network to Operation Technology (OT) as a result of low-cost network expansion. The connection of the OT to the internet for firmware updates, third-party support, or vendor interventions, has exposed the industry to attacks. The inability to detect these unpredictable cyber-attacks exposes the PCN and a successful attack can lead to devastating effects. This paper reviews the different forms of cyber-attacks in PCN of oil and gas installations and proposes the use of machine learning algorithms to monitor data exchanges between the sensors, controllers, processes, and the final control elements on the network so as to detect anomalies in such data exchanges. Python 3.0 Libraries, Deep-Learning Toolkit, MATLAB, and Allen Bradley RSLogic 5000 PLC Emulator software were used in the simulation of process control. The outcome of the experiments shows the reliability and functionality of the different machine-learning algorithms in detecting these anomalies with significant precise attack detections identified using a coarse tree algorithm.

show abstract

An IoT Solution for Air Quality Monitoring and Hazard Identification for Smart City Development

Cited by 3 publications

References 21 publications

Detection of Man-in-the-Middle (MitM) Cyber-Attacks in Oil and Gas Process Control Networks Using Machine Learning Algorithms

Detection of Man-in-the-Middle (MitM) Cyber-Attacks in Oil and Gas Process Control Networks Using Machine Learning Algorithms

An Experimentation on CoAP Multi Factor Authentication Mechanism with Reputation for Internet of Things Constrained Devices and Low Power Wide Area Network

Detection of Man-in-the-Middle (MitM) Cyber-Attacks in Oil and Gas Process Control Networks using Machine Learning Algorithms

Contact Info

Product

Resources

About