A multi-objective framework for the identification and optimisation of factors affecting cybersecurity in the Industry 4.0 supply chain
提出一个框架，利用漏洞数据库和文本处理识别网络风险、威胁和应对措施，并通过多目标优化在风险与投资间取得平衡，帮助供应链管理者制定预防策略。
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.