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@article{177511,
        author = {Anupriya},
        title = {Evaluating Traffic Classification Techniques for Smart City Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {863-868},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177511},
        abstract = {Smart city networks support a wide range of applications, each imposing specific Quality of Service (QoS) requirements, making network management particularly challenging. Despite the need, large-scale deployments of QoS-supporting solutions remain limited. Traffic classification plays a key role in managing network aspects, including QoS assurance. However, traditional traffic classification methods, such as port-based approaches, are increasingly inefficient due to their inability to handle dynamic port allocations and encrypted traffic. Recently, machine learning (ML) has emerged as a promising alternative for traffic classification, offering intelligence that enhances network management. In this study, we apply four supervised ML algorithms—Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Decision Tree (DT)—to predict and classify network traffic.},
        keywords = {machine learning; traffic classification; smart city; quality of service; Internet of things; supervised learning.},
        month = {May},
        }