Details

Autor(en) / Beteiligte

• Shukla, Mayank
• Sarmah, S.P.
• Tiwari, Manoj Kumar

Titel

A multi-objective framework for the identification and optimisation of factors affecting cybersecurity in the Industry 4.0 supply chain

Ist Teil von

• International journal of production research, 2023-08, Vol.61 (15), p.5266-5281

Ort / Verlag

London: Taylor & Francis

Erscheinungsjahr

2023

Quelle

Taylor & Francis Journals Auto-Holdings Collection

Beschreibungen/Notizen

• Digital assets are highly vulnerable and always prone to malicious intervention. Identification of causes of such intervention for timely support and assistance remains a key challenge for businesses to remain functional and thrive with the competition. A framework is proposed in this paper for identifying cyber risk, threat, and countermeasure, based on breach databases and textual information processing. Alongside, a multi-objective optimisation of a mixed-integer non-linear problem (MINLP) is made post linearisation to find out a suitable trade-off between cyber risk and investment. The model helps in effective decision-making by finding the proneness of suppliers (as nodes) in the sequence of reducing vulnerability and pairing of categorised factors. The web scrapping and historical databases are processed to extract relationships among categorised factors using natural language processing (NLP). Pareto optimal pairs are obtained to explain the application of the current contribution in terms of risk-cost trade-off. It helps in forming preventive strategies with a suitable amount of investment and the required order of precedence or susceptibility.