IoT-Based Fraud Detection in Smart Homes
Keywords:
Anomaly detection, Blockchain, Cybersecurity, Data integrity, Deep learning, Ethical AI, Federated learning, Fraud detection, Intrusion detection, IoT, Machine learning, Privacy, Smart homeAbstract
The integration of Internet of Things (IoT) devices into smart homes is transforming modern living by enabling automation, interconnectivity, and seamless control. However, this advancement also introduces significant cybersecurity and fraud risks. Smart home ecosystems are vulnerable to weak authentication, unencrypted data transmission, outdated firmware, and poor network segmentation. These layered risks demand intelligent, adaptive, and privacy-preserving detection mechanisms capable of keeping pace with evolving threats.
As the number of connected home devices such as cameras, smart locks, lighting systems, and digital assistants rapidly increases, the attack surface expands. Cybercriminals can exploit small vulnerabilities, while traditional rule-based detection systems often fail to identify subtle anomalies in device behavior or data flow. To address these challenges, this study proposes a federated, self-learning anomaly detection framework designed for IoT-enabled smart homes. The framework integrates distributed machine learning with blockchain-based data validation to ensure both high detection accuracy and strong data integrity. By keeping user data within the home and sharing only model updates, the system enhances privacy while improving scalability and adaptability.
Experiments on smart home datasets demonstrate the framework’s ability to detect fraudulent and abnormal activities with improved precision and low latency. Beyond identifying common threats like unauthorized access or device hijacking, the model also recognizes complex anomalies such as resource misuse, cryptojacking, and sensor manipulation. The results highlight the promise of combining federated learning and blockchain to build scalable, privacy-aware, and self-evolving fraud detection systems for future smart homes.
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