CyberSCADA Network Security Analysis Model for Intrusion Detection Systems in the Smart Grid

Efiong, John E.; Akinyemi, Bodunde Odunola; Olajubu, Emmanuel Ajayi; Aderounmu, G. A.; Dégila, Jules

doi:10.1007/978-3-031-24475-9_41

Cited by 4 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Water pressure in pipes, water storage levels, and water distribution are all things that may be managed and controlled using SCADA systems, which are employed by state and municipal governments. In a SCADA system, it is typical to have computing workstations, a HMI, PLCs, sensors, and actuators [8][9][10][11]. In the past, these sorts of systems were dependent on solitary and specialized network configurations.…”

Section: Introductionmentioning

confidence: 99%

Design of Efficient Based Artificial Intelligence Approaches for Sustainable of Cyber Security in Smart Industrial Control System

Alzahrani

Aldhyani

2023

Sustainability

View full text Add to dashboard Cite

Online food security and industrial environments and sustainability-related industries are highly confidential and in urgent need for network traffic analysis to attain proper security information to avoid attacks from anywhere in the world. The integration of cutting-edge technology such as the Internet of things (IoT) has resulted in a gradual increase in the number of vulnerabilities that may be exploited in supervisory control and data acquisition (SCADA) systems. In this research, we present a network intrusion detection system for SCADA networks that is based on deep learning. The goal of this system is to defend ICSs against network-based assaults that are both conventional and SCADA-specific. An empirical evaluation of a number of classification techniques including k-nearest neighbors (KNN), linear discriminant analysis (LDA), random forest (RF), convolution neural network (CNN), and integrated gated recurrent unit (GRU) is reported in this paper. The suggested algorithms were tested on a genuine industrial control system (SCADA), which was known as the WUSTL-IIoT-2018 and WUSTL-IIoT-20121 datasets. SCADA system operators are now able to augment proposed machine learning and deep learning models with site-specific network attack traces as a result of our invention of a re-training method to handle previously unforeseen instances of network attacks. The empirical results, using realistic SCADA traffic datasets, show that the proposed machine learning and deep-learning-based approach is well-suited for network intrusion detection in SCADA systems, achieving high detection accuracy and providing the capability to handle newly emerging threats. The accuracy performance attained by the KNN and RF algorithms was superior and achieved a near-perfect score of 99.99%, whereas the CNN-GRU model scored an accuracy of 99.98% using WUSTL-IIoT-2018. The Rf and GRU algorithms achieved >99.75% using the WUSTL-IIoT-20121 dataset. In addition, a statistical analysis method was developed in order to anticipate the error that exists between the target values and the prediction values. According to the findings of the statistical analysis, the KNN, RF, and CNN-GRU approaches were successful in achieving an R2 > 99%. This was demonstrated by the fact that the approach was able to handle previously unknown threats in the industrial control systems (ICSs) environment.

show abstract

Section: Introductionmentioning

confidence: 99%