Authors : Jaden Pereira; Anant Raj

Volume/Issue : Volume 10 - 2025, Issue 7 - July

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Google Scholar : https://tinyurl.com/mwdhskxx

DOI : https://doi.org/10.38124/ijisrt/25jul492

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : With the rapid expansion of Internet of Things (IoT) devices in smart homes, ensuring robust cybersecurity has become a primary concern. Traditional cloud-based threat detection methods struggle with latency, scalability, and privacy issues. This research explores how edge computing, when integrated with artificial intelligence (AI), can significantly enhance real-time threat detection in smart home ecosystems. The study provides an extensive review of existing literature, introduces an experimental setup that compares cloud-based and edge-based models, and analyzes implementation case studies. Key performance metrics such as latency, accuracy, privacy leakage, and bandwidth efficiency are discussed in detail.

Keywords : Edge Computing, Smart Homes, Threat Detection, IoT Security, Anomaly Detection, Federated Learning, Privacy-by- Design, AI at the Edge, Quantum-Resilient Encryption.

References :

1. Adeyeye, O., & Misra, S. (2024). Enhancing data forensics through edge computing in IoT environments. Journal of Network and Computer Applications, 203, 103418. https://doi.org/10.1016/j.jnca.2022.103418
2. Al-Turjman, F., & Malekloo, A. (2022). Fog and edge computing in smart environments: A comparative study. Computer Communications, 182, 53–63.https://doi.org/10.1016/j.comcom.2021.09.015
3. Bhuiyan, M. Z. A., Wu, J., & Wang, G. (2024). A novel edge-based intrusion detection system for smart homes. International Journal of Distributed Sensor Networks, 20(1), 15501477211012345. https://doi.org/10.1177/15501477211012345
4. Hengst, D., & Fischer, M. (2019). Security challenges in IoT-based smart homes and edge computing solutions. Procedia Computer Science, 155, 631–638. https://doi.org/10.1016/j.procs.2019.08.090
5. Li, Y., & Li, J. (2022). Secure IoT network architecture with edge computing and AI-based threat detection. Journal of Information Security and Applications, 64, 103012. https://doi.org/10.1016/j.jisa.2021.103012
6. Narducci, F., & Poggi, A. (2018). AI-based hybrid threat detection models for IoT networks. Computer Networks, 144, 154–166. https://doi.org/10.1016/j.comnet.2018.07.004
7. Nguyen, T. D., & Kim, D. S. (2022). Towards secure and efficient edge-based threat detection in IoT networks. IEEE Internet of Things Journal, 9(5), 3497–3510. https://doi.org/10.1109/JIOT.2021.3081234
8. Singh, A., & Rana, R. (2022). Emerging security standards and protocols for smart home interoperability. Journal of Cybersecurity Practice, 12(3), 112–124.
9. Zeng, E., Mare, S., & Roesner, F. (2017). End user security and privacy concerns with smart homes. In Thirteenth Symposium on Usable Privacy and Security (SOUPS 2017) (pp. 65–80). USENIX. https://www.usenix.org/conference/soups2017/technical-sessions/presentation/zeng
10. Zhao, X., Li, Y., & Choudhury, S. (2023). Federated learning for smart home threat detection. ACM Transactions on Internet Technology, 23(4), 1–21. https://doi.org/10.1145/3594380
11. Zhou, L., Wang, L., & Zhang, J. (2022). Blockchain-enhanced device authentication in edge IoT networks. Future Generation Computer Systems, 128, 74–87. https://doi.org/10.1016/j.future.2021.10.001
12. Binns, R., Lyngs, U., Van Kleek, M., Zhao, J., & Shadbolt, N. (2022). Protecting privacy in smart homes: Challenges and opportunities. Sensors, 22(3), 987.https://doi.org/10.3390/s22030987