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<p>The rapid growth of Internet of Things (IoT) devices in smart grids and industrial control systems means that the global state has attained a level of technological evolution. Yet this growth has also created an enormous attack surface with millions of vulnerable endpoints, thus revealing inherent weaknesses of traditional security models. These conventional systems are fraught with data integrity challenges, single points of failure, and no proactive defense against new, adaptive cyber-physical threats. To overcome these limitations, this paper presents "Causio-TwinChain," a new security model that synergistically integrates three leading-edge technologies to establish a proactive, self-diagnostic, and tamper-resistant security framework for critical IoT infrastructure. A digital twin is a virtual replica that can monitor physical devices in real time via sandboxing. A permissioned blockchain provides an immutable, tamper-proof ledger for all device data and transactions, ensuring data integrity and auditability. Two kinds of machine-learning engines form the core intelligence: contrastive Learning, which detects subtle anomalies by modeling normal operations; and structural causal Learning, which diagnoses root causes of security incidents and predicts their potential impact. The model’s superior efficacy is demonstrated on an industrial IoT dataset. Causio-TwinChain yielded a 15.3% higher F1-score in novel attack detection, and reduced the mean time for incident diagnosis by 68% compared to benchmark intrusion detection systems. This model reduced the false-positive rate by 22%, demonstrating its robustness in noisy environments. Moving beyond mere attack detection to explainable diagnosis and predictive mitigation, this work establishes a new benchmark for building proactive, resilient, and self-healing security frameworks that safeguard the most critical IoT applications and enhance trust and continuity in operational services.</p>

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Digital twin-assisted blockchain IoT security model using contrastive and causal learning techniques

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月18日

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  • Ashit Kumar Dutta,
  • Mohd Anjum,
  • Hong Min,
  • Yousef Ibrahim Daradkeh,
  • Jung Taek Seo,
  • Sana Shahab

摘要

The rapid growth of Internet of Things (IoT) devices in smart grids and industrial control systems means that the global state has attained a level of technological evolution. Yet this growth has also created an enormous attack surface with millions of vulnerable endpoints, thus revealing inherent weaknesses of traditional security models. These conventional systems are fraught with data integrity challenges, single points of failure, and no proactive defense against new, adaptive cyber-physical threats. To overcome these limitations, this paper presents "Causio-TwinChain," a new security model that synergistically integrates three leading-edge technologies to establish a proactive, self-diagnostic, and tamper-resistant security framework for critical IoT infrastructure. A digital twin is a virtual replica that can monitor physical devices in real time via sandboxing. A permissioned blockchain provides an immutable, tamper-proof ledger for all device data and transactions, ensuring data integrity and auditability. Two kinds of machine-learning engines form the core intelligence: contrastive Learning, which detects subtle anomalies by modeling normal operations; and structural causal Learning, which diagnoses root causes of security incidents and predicts their potential impact. The model’s superior efficacy is demonstrated on an industrial IoT dataset. Causio-TwinChain yielded a 15.3% higher F1-score in novel attack detection, and reduced the mean time for incident diagnosis by 68% compared to benchmark intrusion detection systems. This model reduced the false-positive rate by 22%, demonstrating its robustness in noisy environments. Moving beyond mere attack detection to explainable diagnosis and predictive mitigation, this work establishes a new benchmark for building proactive, resilient, and self-healing security frameworks that safeguard the most critical IoT applications and enhance trust and continuity in operational services.