A data-driven approach to router-level security in supply chain digitalization

Increasing digitalization has made supply chains more digitally connected. However, the connectivity between supply chain partners has also made supply chains much more complex and vulnerable to cyber-attacks. While attacks against routers could impact the integrity of supply chain data and negatively affect the reliability of supply chain networks, by employing an artificial intelligence (AI) based intrusion detection and routing system, supply chain networks can create a protective response against router-level integrity threats. This type of AI-based solution would provide the capability to detect unusual network activities early on in a supply chain network and leverage that knowledge to inform routing decisions when there are any potential cyber threats. Rather than treating intrusion detection systems and routing systems as two independent components of supply chain security, this research evaluates the integration of both into a single process that can yield the most optimized level of security and operational efficiency. By combining intrusion detection actions and routing decisions into a single process of action, organizations would be able to streamline security actions to be congruent with their specific supply chain networking objectives rather than viewing security actions as individual activities. In this regard, the simulation experiment proposed in this paper would be conducted using various scenarios of supply chain networks to test the combined processes of intrusion detection and routing against cyber threats. The results reveal a positive outcome in terms of detection, along with stable routes, where the intrusion detection system would reach a detection rate of 95% with minimized false alarms. Furthermore, safe access to devices would be ensured through a decentralized authentication system. • Present secure router analytics that strengthen resilience in digital supply chains. • Integrate management strategies into router security to protect public value. • Apply data-driven analytics to improve the detection of threats in supply chains. • Demonstrate router-level integration that enhances digital infrastructure resilience. • Provide practical insights for secure and resilient supply chain networks.