Security Framework to Detect Drive-By Infection on Smart Home IOT Devices

Oluwashina Rasak Yusuff; Dr Musa Sule Argungu; Dr Muhammad Saidu Aliero

doi:10.56201/ijcsmt.vol.11.no4.2025.pg.1.7

📄 Download Paper

Security Framework to Detect Drive-By Infection on Smart Home IOT Devices

Oluwashina Rasak Yusuff, Dr Musa Sule Argungu, Dr Muhammad Saidu Aliero

Abstract

The integration of smart home Internet of Things (IoT) devices into daily life has created opportunities for enhanced convenience and automation; however, it also exposes these devices to significant security threats, particularly drive-by infections. This study proposes a security framework specifically designed to detect and mitigate drive-by infections in smart home environments. The framework adopts a hybrid detection technique combining anomaly detection, behavioral analysis, and signature-based methods while leveraging lightweight algorithms such as Isolation Forest and One-Class SVM. Results demonstrate a high detection rate exceeding 90%, with notable reductions in false positives compared to existing methods. This research presents an efficient and scalable model that enhances the security of smart home IoT devices, safeguarding user privacy and device integrity.

Keywords

Smart Home IoTDrive-By InfectionsSecurity FrameworkAnomaly Detection

References

Akashdeep, Bhardwaj. (2024). Smart Home and Industrial IoT Devices: Critical Perspectives on Cyberthreats, Frameworks and Protocols. doi: 10.2174/97898152567101240101 Ashawa, M., & Morris, S. (2021). Analysis of Mobile Malware: A Systematic Review of Evolution and Infection Strategies. Journal of Information Security and Cybercrimes Research, 4(2), 103–131. Azis, B., Ong, A. K. S., Prasetyo, Y. T., Persada, S. F., Young, M. N., Sari, Y. K. P., & Nadlifatin, R. (2023). IoT human needs inside compact house. Journal of Open Innovation: Technology, Market, and Complexity, 9(1), 1-9 Benaroch, Michel. (2021). Third-party induced cyber incidents—much ado about nothing?. Journal of Cybersecurity. 7. 10.1093/cybsec/tyab020. Bugeja, J., Jacobsson, A., & Davidsson, P. (2018). Smart connected homes. Internet of things A to Z: Technologies and applications, 359-384. Kounoudes, A. D., & Kapitsaki, G. M. (2020). A mapping of IoT user-centric privacy-preserving approaches to the GDPR. Internet of Things, 11, https://doi.org/10.1016/j.iot.2020.100179 Nkenyereye, L., Hwang, J., Pham, Q. V., & Song, J. (2021). Virtual IoT service slice functions for multiaccess edge computing platform. IEEE Internet of Things Journal, 8(14), 11233- Omar, Alshamsi., Khaled, Shaalan., Usman, Javed, Butt. (2024). Towards Securing Smart Homes: A Systematic Literature Review of Malware Detection Techniques and Recommended Prevention Approach. Information, 15(10):631-631. doi: 10.3390/ info15100631 Prabakaran, M. K., Meenakshi Sundaram, P., & Chandrasekar, A. D. (2023). An enhanced deep learning?based phishing detection mechanism to effectively identify malicious URLs using variational autoencoders. IET Information Security, 17(3), 423-440 Sebastian Garcia, Agustin Parmisano, & Maria Jose Erquiaga. (2020). IoT-23: A labeled dataset with malicious and benign IoT network traffic (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4743746 Shobana, M., & Rathi, S. (2018). Iot malware: An analysis of iot device hijacking. International Journal of Scientific Research in Computer Science, Computer Engineering, and Information Technology, 3(5), 2456-3307 Song, M. (2023, September 15). Understanding the Confusion Matrix Without Confusion. Medium. https://medium.com/@msong507/understanding-the-confusion-matrix-without confusion-126b25dd773c Vadigi, S., Sethi, K., Mohanty, D., Das, S. P., & Bera, P. (2023). Federated reinforcement learning- based intrusion detection system using dynamic attention mechanism. Journal of Information Security and Applications, 78, https://doi.org/10.1016/j.jisa.2023.103608 Zrelli, A. (2022). Hardware, software platforms, operating systems and routing protocols for Internet of Things applications. Wireless Personal Communications, 122(4), 3889-3912

Security Framework to Detect Drive-By Infection on Smart Home IOT Devices

Abstract

Keywords

References

More Articles from INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY