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

Journal article


Gaba, S., Budhiraja, S., Kumar, V. and Makkar, A. Advancements in enhancing cyber-physical system security: Practical deep learning solutions for network traffic classification and integration with security technologies. Communications in Analysis and Mechanics. 21 (1), pp. 1527-155. https://doi.org/10.3934/mbe.2024066
AuthorsGaba, S., Budhiraja, S., Kumar, V. and Makkar, A.
Abstract

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.

Keywordsnetwork traffic classification; machine learning; deep learning; hybrid model
JournalCommunications in Analysis and Mechanics
Journal citation21 (1), pp. 1527-155
PublisherAIMS Press
ISSN 2836-3310
Digital Object Identifier (DOI)https://doi.org/10.3934/mbe.2024066
Web address (URL)https://www.aimspress.com/article/doi/10.3934/mbe.2024066
Output statusPublished
Publication dates
Online29 Dec 2023
Publication process dates
Deposited12 Feb 2024
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