Protocol study and anomaly detection for server-driven traffic in SCADA networks

Lin, Chih‐Yuan; Nadjm‐Tehrani, Simin

doi:10.1016/j.ijcip.2023.100612

Cited by 6 publications

(1 citation statement)

References 20 publications

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“…One of the key technologies that play an important role in the transformation of Industry 4.0 is cloud computing, big data, and data analysis (Khan et al, 2023). The data processing results will come on stage in Supervisory Control and Data Acquisition (SCADA) as activity process control in the industry (Lin and Nadjm-Tehrani, 2023). The whole activity will be monitored by a working system for tracking and collecting relevant real-time data with the production process goods manufacturing in the so-called industry with the Manufacturing Execution System (MES) (Peiris et al, 2023).…”

Section: Literature Review Industry 40mentioning

confidence: 99%

Evaluation of Human Resources Pillars in Industry Readiness Index to Transform towards Industry 4.0

Sayekti,

Suhariadi,

Herachwati

2023

bmss

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Developed countries have implemented Industry 4.0 in the process and manufacturing industry sectors. Industry 4.0 transformation will positively impact the industry and the country. Indonesia introduced a guide for the industry in transforming to Industry 4.0 in its action for Making Indonesia 4.0. The Indonesia Industry 4.0 Readiness Index (INDI 4.0) is presented to assist the industry in carrying out transformations, with the pillar of human resources as the highest parameter. It is necessary to conduct an early evaluation of the industry to introduce INDI 4.0. This research aims to evaluate the level of industry readiness in transforming to Industry 4.0. The method in this study used a survey of Small and Medium Industries (SMI=IKM) in Bogor City. Three SMIs were selected for interviews according to INDI 4.0 guidelines and Toolbox Industrie 4.0. The evaluation results using the INDI 4.0 guidelines show that SMI A has a higher level of readiness, followed by SMI C and B. In line with the results of the INDI 4.0 assessment, Toolbox Industrie 4.0 also has the same results. SMI A has a higher level of readiness, followed by SMI C and B. In Toolbox Industrie 4.0, there is no element of assessment for human resources due to cultural differences between Germany and Indonesia, which still require special attention for human resources.

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Section: Literature Review Industry 40mentioning

confidence: 99%

Evaluation of Human Resources Pillars in Industry Readiness Index to Transform towards Industry 4.0

Sayekti,

Suhariadi,

Herachwati

2023

bmss

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Oversampling and undersampling for intrusion detection system in the supervisory control and data acquisition IEC 60870‐5‐104

Arifin,

Stiawan,

Yudho Suprapto

et al. 2024

IET Cyber-Phy Sys Theory & Ap

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Supervisory control and data acquisition systems are critical in Industry 4.0 for controlling and monitoring industrial processes. However, these systems are vulnerable to various attacks, and therefore, intelligent and robust intrusion detection systems as security tools are necessary for ensuring security. Machine learning‐based intrusion detection systems require datasets with balanced class distribution, but in practice, imbalanced class distribution is unavoidable. A dataset created by running a supervisory control and data acquisition IEC 60870‐5‐104 (IEC 104) protocol on a testbed network is presented. The dataset includes normal and attacks traffic data such as port scan, brute force, and Denial of service attacks. Various types of Denial of service attacks are generated to create a robust and specific dataset for training the intrusion detection system model. Three popular techniques for handling class imbalance, that is, random over‐sampling, random under‐sampling, and synthetic minority oversampling, are implemented to select the best dataset for the experiment. Gradient boosting, decision tree, and random forest algorithms are used as classifiers for the intrusion detection system models. Experimental results indicate that the intrusion detection system model using decision tree and random forest classifiers using random under‐sampling achieved the highest accuracy of 99.05%. The intrusion detection system model's performance is verified using various metrics such as recall, precision, F1‐Score, receiver operating characteristics curves, and area under the curve. Additionally, 10‐fold cross‐validation shows no indication of overfitting in the created intrusion detection system model.

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A Review Paper: Security for Supervisory Control and Data Acquisition SCADA Based on DNP3

Jasim,

Alheeti

2023

2023 16th International Conference on Developments in eSystems Engineering (DeSE)

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Protocol study and anomaly detection for server-driven traffic in SCADA networks

Cited by 6 publications

References 20 publications

Evaluation of Human Resources Pillars in Industry Readiness Index to Transform towards Industry 4.0

Evaluation of Human Resources Pillars in Industry Readiness Index to Transform towards Industry 4.0

Oversampling and undersampling for intrusion detection system in the supervisory control and data acquisition IEC 60870‐5‐104

A Review Paper: Security for Supervisory Control and Data Acquisition SCADA Based on DNP3

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