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Related Concept Videos

Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Related Experiment Videos

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

Ashit Kumar Dutta1,2, Mohd Anjum3, Hong Min4

  • 1Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, 13713, Diriyah, Riyadh, Saudi Arabia.

Scientific Reports
|April 18, 2026
PubMed
Summary
This summary is machine-generated.

Causio-TwinChain enhances Internet of Things (IoT) security by integrating digital twins, blockchain, and AI. This proactive model improves attack detection and incident diagnosis for critical infrastructure.

Keywords:
Contrastive anomaly detectionDigital twinIndustrial IoT securityIntrusion detection systemsPermissioned blockchainStructural causal learning

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Distributed Ledger Technology

Background:

  • Rapid expansion of Internet of Things (IoT) devices in critical infrastructure has exposed vulnerabilities in traditional security models.
  • Conventional systems lack robust defense against sophisticated cyber-physical threats, facing challenges in data integrity and single points of failure.
  • Need for advanced security frameworks that are proactive, self-diagnostic, and tamper-resistant for industrial control systems and smart grids.

Purpose of the Study:

  • To introduce Causio-TwinChain, a novel security model designed for critical IoT infrastructure.
  • To synergistically integrate digital twins, permissioned blockchain, and advanced machine learning for enhanced security.
  • To establish a proactive, self-diagnostic, and tamper-resistant security framework.

Main Methods:

  • Utilizing digital twins for real-time monitoring of physical devices through sandboxing.
  • Employing a permissioned blockchain to ensure an immutable ledger for device data and transactions, guaranteeing integrity and auditability.
  • Implementing two machine learning engines: contrastive learning for anomaly detection and structural causal learning for root cause analysis and impact prediction.

Main Results:

  • Causio-TwinChain demonstrated a 15.3% higher F1-score in detecting novel attacks compared to benchmark systems.
  • The mean time for incident diagnosis was reduced by 68% through the model's causal learning capabilities.
  • A 22% reduction in the false-positive rate was observed, highlighting the model's robustness in complex environments.

Conclusions:

  • Causio-TwinChain offers a significant advancement beyond traditional intrusion detection systems by providing explainable diagnosis and predictive mitigation.
  • The integrated approach establishes a new benchmark for proactive, resilient, and self-healing security frameworks in critical IoT applications.
  • This model enhances trust and ensures continuity of operational services in the face of evolving cyber-physical threats.