Cyber resilience in supply chain system security using machine learning for threat predictions

Abstract

PurposeCyber resilience in cyber supply chain (CSC) systems security has become inevitable as attacks, risks and vulnerabilities increase in real-time critical infrastructure systems with little time for system failures. Cyber resilience approaches ensure the ability of a supply chain system to prepare, absorb, recover and adapt to adverse effects in the complex CPS environment. However, threats within the CSC context can pose a severe disruption to the overall business continuity. The paper aims to use machine learning (ML) techniques to predict threats on cyber supply chain systems, improve cyber resilience that focuses on critical assets and reduce the attack surface.Design/methodology/approachThe approach follows two main cyber resilience design principles that focus on common critical assets and reduce the attack surface for this purpose. ML techniques are applied to various classification algorithms to learn a dataset for performance accuracies and threats predictions based on the CSC resilience design principles. The critical assets include Cyber Digital, Cyber Physical and physical elements. We consider Logistic Regression, Decision Tree, Naïve Bayes and Random Forest classification algorithms in a Majority Voting to predicate the results. Finally, we mapped the threats with known attacks for inferences to improve resilience on the critical assets.FindingsThe paper contributes to CSC system resilience based on the understanding and prediction of the threats. The result shows a 70% performance accuracy for the threat prediction with cyber resilience design principles that focus on critical assets and controls and reduce the threat.Research limitations/implicationsTherefore, there is a need to understand and predicate the threat so that appropriate control actions can ensure system resilience. However, due to the invincibility and dynamic nature of cyber attacks, there are limited controls and attributions. This poses serious implications for cyber supply chain systems and its cascading impacts.Practical implicationsML techniques are used on a dataset to analyse and predict the threats based on the CSC resilience design principles.Social implicationsThere are no social implications rather it has serious implications for organizations and third-party vendors.Originality/valueThe originality of the paper lies in the fact that cyber resilience design principles that focus on common critical assets are used including Cyber Digital, Cyber Physical and physical elements to determine the attack surface. ML techniques are applied to various classification algorithms to learn a dataset for performance accuracies and threats predictions based on the CSC resilience design principles to reduce the attack surface for this purpose.

Mitigating cybercrimes in an evolving organizational landscape

“…Most organizations are integrated into a supply chain systems environment as part of their evolving nature, thus requiring cyber resilience and cybercrime mitigation techniques to ensure business continuity. For instance, Yeboah-Ofori et al (2022) proposed a cyber resilience approach focusing on common critical assets using ML techniques and threat prediction to reduce the attack surface. The paper does not consider evolving organizations as an entity but rather from an integrated and supply chain system perspective.…”

“…Consequently, these new trends in electronic products and services that the banking industries are using have also brought about a lot of vulnerabilities, threats and attacks to extraordinary levels. Thus, mitigating cybercrimes could provide a cyber resilience environment for organizations to understand the threat landscape and gain situational awareness in the supply chain environment to ensure business continuity (Yeboah-Ofori et al, 2022). For instance, Camillo et al (2012) posit that evolving organizational requirements and varying organizational business process requirements and the continued adoption of web applications, mobile, cloud and social media technologies to facilitate business processes have in recent times, increased opportunities for attackers in terms of online purchases, payments using card payments (Camillo et al, 2012).…”

“…The cyberspace and Internet provides organizations the capabilities to evolve and integrate their network system with other organizations and third-party vendors to transact online business locally and globally as it removes barriers and maximizes time to market in a cyber supply chain environment (Pawar and Palivela, 2022; Yeboah-Ofori et al ., 2022). Further, the Covid-19 pandemic has heightened the need for increased dependency on the Internet by organizations to facilitate their business processes and provide greater reach (Thomas and Sule, 2022).…”

“…Consequently, these new trends in electronic products and services that the banking industries are using have also brought about a lot of vulnerabilities, threats and attacks to extraordinary levels. Thus, mitigating cybercrimes could provide a cyber resilience environment for organizations to understand the threat landscape and gain situational awareness in the supply chain environment to ensure business continuity (Yeboah-Ofori et al ., 2022). For instance, Camillo et al .…”

Secure Change Control for Supply Chain Systems via Dynamic Event Triggered Using Reinforcement Learning under DoS Attacks

“…A federated learning-based efficient detection model named DFF-SC4N is addressed in [32] to identify intrusions from supply chain 4.0 networks. In the prediction detection for supply chain systems, Logistic Regression, Decision Tree, Naïve Bayes, and Random Forest classification algorithms are considered to learn a dataset for performance accuracies and threat predictions based on the CSC resilience design principles in [33]. However, RL is the only ML technique that can learn without any dataset.…”