Enhancing Cybersecurity in SCADA IoT Systems: A Novel Machine Learning-Based Approach for Man-in-the-Middle Attack Detection

Mughaid, Ala; AlJamal, Mohammad; AL-Aiash, Issa; AlJamal, Mahmoud; Alquran, Rabee; AlZu'bi, Shadi; Abutabanjeh, Ala A.

doi:10.1109/icsc60084.2023.10349993

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Cited by 2 publications

References 16 publications

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Simulated Model for Preventing IoT Fake Clients over the Smart Cities Environment

Aljamal,

Mughaid,

Alquran

et al. 2023

2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf

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Simulated Model for Preventing IoT Fake Clients over the Smart Cities Environment

Aljamal,

Mughaid,

Alquran

et al. 2023

2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf

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Advancing XSS Detection in IoT over 5G: A Cutting-Edge Artificial Neural Network Approach

Alqura’n,

AlJamal,

Al-Aiash

et al. 2024

IoT

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The rapid expansion of the Internet of Things (IoT) and the advancement of 5G technology require strong cybersecurity measures within IoT frameworks. Traditional security methods are insufficient due to the wide variety and large number of IoT devices and their limited computational capabilities. With 5G enabling faster data transmission, security risks have increased, making effective protective measures essential. Cross-Site Scripting (XSS) attacks present a significant threat to IoT security. In response, we have developed a new approach using Artificial Neural Networks (ANNs) to identify and prevent XSS breaches in IoT systems over 5G networks. We significantly improved our model’s predictive performance by using filter and wrapper feature selection methods. We validated our approach using two datasets, NF-ToN-IoT-v2 and Edge-IIoTset, ensuring its strength and adaptability across different IoT environments. For the NF-ToN-IoT-v2 dataset with filter feature selection, our Bilayered Neural Network (2 × 10) achieved the highest accuracy of 99.84%. For the Edge-IIoTset dataset with filtered feature selection, the Trilayered Neural Network (3 × 10) achieved the best accuracy of 99.79%. We used ANOVA tests to address the sensitivity of neural network performance to initial conditions, confirming statistically significant improvements in detection accuracy. The ANOVA results validated the enhancements across different feature selection methods, demonstrating the consistency and reliability of our approach. Our method demonstrates outstanding accuracy and robustness, highlighting its potential as a reliable solution for enhancing IoT security in the era of 5G networks.

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Enhancing Cybersecurity in SCADA IoT Systems: A Novel Machine Learning-Based Approach for Man-in-the-Middle Attack Detection

Cited by 2 publications

References 16 publications

Simulated Model for Preventing IoT Fake Clients over the Smart Cities Environment

Simulated Model for Preventing IoT Fake Clients over the Smart Cities Environment

Advancing XSS Detection in IoT over 5G: A Cutting-Edge Artificial Neural Network Approach

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