An industrial virus propagation model based on SCADA system

Zhu, Qingyi; Zhang, Gang; Luo, Xuhang; Gan, Chenquan

doi:10.1016/j.ins.2022.12.119

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…Zhu Q etc. uses a dual intrusion detection method to detect malware attacks in industrial control coupling networks [5] .Quarantine is another method to protect susceptible hosts. Once a susceptible hosts is found, it is quarantined to install patches or antivirus to make them immune to malicious code [6] .…”

Section: Related Workmentioning

confidence: 99%

Modeling of malicious code propagation in the industrial control network

Yang,

Liu,

Yan

et al. 2024

E3S Web Conf.

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With the development of digitalization, industrial control network have more connections and open to external network, which breaks their “isolation” from Internet and makes them more vulnerable to be attacked especially by malicious code. To model and analyze the impact of different containment strategies for attacks by malicious code in the industrial control network, the SUIQMR model is established to simulate malicious code propagation. Since the industrial control network is often coupled with Internet, industrial control coupling network is also considered in the SUIQMR model and the containment strategies of quarantine,benign worms and honeypot are added into the model to evaluate their effect against malicious code propagation.The simulation results show the effectiveness of our model.

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Section: Related Workmentioning

confidence: 99%

Modeling of malicious code propagation in the industrial control network

Yang,

Liu,

Yan

et al. 2024

E3S Web Conf.

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“…However, these datasets are not adequate to protect the system against new types of attacks since malware changes and attack strategies vary. As a consequence, the benchmark datasets need to be updated on a regular basis [12,13].…”

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

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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.

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