Research article

Advancements in enhancing cyber-physical system security: Practical deep learning solutions for network traffic classification and integration with security technologies

  • Received: 01 September 2023 Revised: 05 December 2023 Accepted: 06 December 2023 Published: 29 December 2023
  • Traditional network analysis frequently relied on manual examination or predefined patterns for the detection of system intrusions. As soon as there was increase in the evolution of the internet and the sophistication of cyber threats, the ability for the identification of attacks promptly became more challenging. Network traffic classification is a multi-faceted process that involves preparation of datasets by handling missing and redundant values. Machine learning (ML) models have been employed to classify network traffic effectively. In this article, we introduce a hybrid Deep learning (DL) model which is designed for enhancing the accuracy of network traffic classification (NTC) within the domain of cyber-physical systems (CPS). Our novel model capitalizes on the synergies among CPS, network traffic classification (NTC), and DL techniques. The model is implemented and evaluated in Python, focusing on its performance in CPS-driven network security. We assessed the model's effectiveness using key metrics such as accuracy, precision, recall, and F1-score, highlighting its robustness in CPS-driven security. By integrating sophisticated hybrid DL algorithms, this research contributes to the resilience of network traffic classification in the dynamic CPS environment.

    Citation: Shivani Gaba, Ishan Budhiraja, Vimal Kumar, Aaisha Makkar. Advancements in enhancing cyber-physical system security: Practical deep learning solutions for network traffic classification and integration with security technologies[J]. Mathematical Biosciences and Engineering, 2024, 21(1): 1527-1553. doi: 10.3934/mbe.2024066

    Related Papers:

  • Traditional network analysis frequently relied on manual examination or predefined patterns for the detection of system intrusions. As soon as there was increase in the evolution of the internet and the sophistication of cyber threats, the ability for the identification of attacks promptly became more challenging. Network traffic classification is a multi-faceted process that involves preparation of datasets by handling missing and redundant values. Machine learning (ML) models have been employed to classify network traffic effectively. In this article, we introduce a hybrid Deep learning (DL) model which is designed for enhancing the accuracy of network traffic classification (NTC) within the domain of cyber-physical systems (CPS). Our novel model capitalizes on the synergies among CPS, network traffic classification (NTC), and DL techniques. The model is implemented and evaluated in Python, focusing on its performance in CPS-driven network security. We assessed the model's effectiveness using key metrics such as accuracy, precision, recall, and F1-score, highlighting its robustness in CPS-driven security. By integrating sophisticated hybrid DL algorithms, this research contributes to the resilience of network traffic classification in the dynamic CPS environment.



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