CYBER THREAT RESILIENCE IN LOGISTICS CHAINS: A SYSTEMATIC REVIEW AND CONCEPTUAL DETECTION FRAMEWORKS

Abstract

The rapid digitalization of logistics and supply chain ecosystems has significantly improved operational visibility, automation, real-time tracking, inventory coordination, and global connectivity. However, increasing dependence on interconnected information systems, Internet of Things (IoT) devices, cloud platforms, application programming interfaces, warehouse automation, transportation management systems, third-party software, and digital communication networks has simultaneously expanded the cyberattack surface of modern logistics chains. Cyber threats such as ransomware, phishing, distributed denial-of-service attacks, malware, credential compromise, supply chain infiltration, data manipulation, insider threats, and IoT exploitation can disrupt transportation operations, compromise sensitive information, manipulate shipment records, and create cascading failures across interconnected organizations. This research presents a systematic review of cyber threat resilience in logistics chains and proposes a conceptual multi-layer detection framework for identifying, analyzing, and responding to cyber threats across digitally connected logistics environments. The proposed framework integrates multi-source telemetry acquisition, security information and event management, network behavior analytics, machine learning-based anomaly detection, threat intelligence correlation, graph-based dependency analysis, risk scoring, and automated incident response. A hybrid detection engine combines signature-based identification with behavioral and anomaly-driven analytics to recognize both known and previously unseen threats. The conceptual framework further introduces logistics-context awareness by correlating cyber indicators with operational information such as shipment status, route deviations, warehouse activities, IoT sensor events, and third-party access patterns. Systematic synthesis of prior studies identifies major research gaps related to fragmented security monitoring, weak third-party visibility, insufficient cross-organizational intelligence sharing, limited explainability, and inadequate cyber-physical correlation. Comparative conceptual evaluation indicates that the proposed integrated framework can improve detection accuracy, resilience coverage, response efficiency, and operational continuity compared with conventional rule-based and isolated monitoring approaches. The study provides a scalable foundation for cyber-resilient logistics ecosystems capable of supporting proactive detection, coordinated response, adaptive recovery, and continuous resilience improvement.