Advanced International Journal for Research

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Design of a Formal Language-Based Traffic Classification System for Smart Cloud Networks

Author(s) Ms. HARSHITHA PRIYAA G R, Mr. KAVIARASAN M, Ms. NEHA NASSAR N
Country India
Abstract The fast rate of data flow, virtualization as well as multi-tenant traffic in today cloud environment has posed new burdens to precise identification, categorization, and control of network traffic. Conventional traffic classification schemes heavily rely on statistical or machine learning schemes that, although strong, are not structurally deterministic or interpretable in the analysis of complicated and mixed-protocol streams of data. In this paper, a new Formal Language-Based Traffic Classification System (FLTCS) that will be used to monitor Smart Cloud Networks is presented, which is based on Language Theory and in particular, Context-Free Grammar (CFG) and Chomsky Normal Form (CNF) to describe and analyze network traffic patterns. The system ciphers packet sequences into the tokenized forms and performs grammar guided parsing to check, categorize, and spot the unusual traffic flows. The proposed framework, implemented in the framework of a modular design, which comprises a cloud traffic analyzer, grammar engine, and intelligent rule controller, allows conducting deterministic verification, effective classification, and flexible rule evolution. Experimental findings on synthetic and real cloud images show that FLTCS performs classification with high accuracy (more than 96.8%), with lower false positive, and lower the rate of rule conflict by 34 percent in comparison to the traditional ML-based classifiers. The suggested solution demonstrates that formal language theory will be a robust, explainable, and verifiable basis of the next generation of intelligent cloud traffic management systems.
Field Computer > Network / Security
Published In Volume 6, Issue 6, November-December 2025
Published On 2025-12-15
DOI https://doi.org/10.63363/aijfr.2025.v06i06.1994
Short DOI https://doi.org/hbf95x
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