Advancements in enhancing cyber-physical system security: Practical deep learning solutions for network traffic...

Area of Science:

• Cybersecurity
• Computer Science
• Network Engineering

Background:

• Traditional network analysis struggles with sophisticated cyber threats.
• Network traffic classification (NTC) is crucial for cybersecurity but complex.
• Machine learning (ML) models offer potential for effective NTC.

Purpose of the Study:

• To introduce a novel hybrid Deep Learning (DL) model for enhanced NTC in cyber-physical systems (CPS).
• To improve the accuracy and robustness of network security within CPS environments.
• To leverage the integration of DL, NTC, and CPS for advanced threat detection.

Main Methods:

• Developed and implemented a hybrid DL model in Python.
• Focused on NTC within the specific domain of CPS.
• Evaluated model performance using accuracy, precision, recall, and F1-score.

Main Results:

• The hybrid DL model demonstrated enhanced accuracy in NTC for CPS.
• The model proved robust in CPS-driven network security applications.
• Key performance metrics confirmed the model's effectiveness.

Conclusions:

• The proposed hybrid DL model significantly improves NTC accuracy in CPS.
• This research contributes to the resilience of network traffic classification in dynamic CPS environments.
• The findings support the advancement of cybersecurity in interconnected systems.