IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 11 NO. 4 2025
DOI: 10.56201/ijcsmt.vol.11.no4.2025.pg.1.7

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

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

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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 IoT, Drive-By Infections, Security Framework, Anomaly Detection,

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