ACS Applied Computer Science

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Enhanced IoT cybersecurity through machine learning - based penetration testing

Mohammed J. BAWANEH, Obaida M. AL-HAZAIMEH, Malek M. AL-NAWASHI, Monther H. AL-BSOOL Essam HANANDAH
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The Internet of Things (IoT) is a new technology that builds on the old Internet. A network connects all objects using technologies such as Radio Frequency Identification (RFID), sensors, GPS, or Machine-to-Machine (M2M) communication. The development of IoT has been negatively impacted by security concerns, which has led to a significant increase in research interest. However, very few methods look at the security of IoT from the attacker's point of view. As of today, penetration testing is a common way to check the security of traditional internet or systems. It usually takes a lot of time and money. In this paper, we look at the security problems of the Internet of Things (IoT) and suggest a way to test for them. This way is based on a combination of the belief-desire intention (BDI) model and machine learning. The results of the experiments showed that they were very good at detecting and defending against cyberattacks on IoT devices. The proposed BDI-based recall method provided 85% of the results. The 90% precision suggests that the measurements are very accurate. The F1-score was 87.4%, and the accuracy was 95%. The proposed BDI is of exceptional quality in every part of the penetration-testing model.  Therefore, it is possible to create a system that can detect and defend against cyberattacks based on the proposed BDI model.

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  • APA 7th style
Bawaneh, M. J., Al-Hazaimeh, O. M., Al-Nawashi, M. M., Al-Bsool, M. H., & Hanandah, E. (2025). Enhanced IoT cybersecurity through Machine Learning—Based penetration testing. Applied Computer Science, 21(2), 96–110. https://doi.org/10.35784/acs_7397
  • Chicago style
Bawaneh, Mohammed J., Obaida M. Al-Hazaimeh, Malek M. Al-Nawashi, Monther H. Al-Bsool, and Essam Hanandah. ‘Enhanced IoT Cybersecurity through Machine Learning - Based Penetration Testing’. Applied Computer Science 21, no. 2 (2025): 96–110. https://doi.org/10.35784/acs_7397.
  • IEEE style
M. J. Bawaneh, O. M. Al-Hazaimeh, M. M. Al-Nawashi, M. H. Al-Bsool, and E. Hanandah, ‘Enhanced IoT cybersecurity through Machine Learning - based penetration testing’, Applied Computer Science, vol. 21, no. 2, pp. 96–110, doi: 10.35784/acs_7397.
  • Vancouver style
Bawaneh MJ, Al-Hazaimeh OM, Al-Nawashi MM, Al-Bsool MH, Hanandah E. Enhanced IoT cybersecurity through Machine Learning - based penetration testing. Applied Computer Science. 2025; 21(2):96–110.

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