Preventing Data Integrity Breaches in IoT Applications Using Digital Twins
Mohammed Ibrahim El-Hajj · 2025
The proliferation of Internet of Things (IoT) devices in critical sectors like healthcare, manufacturing, and smart cities has heightened vulnerabilities to data integrity breaches, where malicious actors tamper with sensor data or communication channels. Traditional security mechanisms, such as encryption and firewalls, often fail to address dynamic IoT-specific threats like replay attacks and sensor spoofing. This paper proposes digital twins—virtual replicas of physical IoT systems—as a robust solution for real-time anomaly detection, data validation, and attack simulation. Through simulations involving platforms like Azure Digital Twins, AWS IoT Twin-Maker, and open-source frameworks (Eclipse Ditto, Thing-Work), we demonstrate that digital twins reduce attack success rates by 70% and detect anomalies within 150 milliseconds, outperforming traditional intrusion detection systems by 76.8% in latency. Key contributions include a scalable hybrid architecture for edge-cloud deployments and quantifiable metrics for attack mitigation. Our findings highlight the potential of digital twins to safeguard industrial IoT applications, with implications for predictive maintenance and regulatory compliance. Challenges such as computational overhead and model evasion tactics are discussed, alongside future directions for lightweight implementations and AI/ML-enhanced anomaly detection.