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@article{161140,
        author = {R. Kavitha and P. Saranya},
        title = {CLASSIFICATION OF IOT NETWORK TRAFFIC USING MACHINE LEARNING ALGORITHMS},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {2},
        pages = {669-675},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=161140},
        abstract = {The identification of IoT devices in network data is one such essential operation. It enables the administrator to keep monitors on the actions of IoT devices, which can be helpful for the effective implementation of Quality of Service, detect malicious IoT devices, etc. In this study, a machine learning based classification of IoT network traffic is proposed. IoT traffic classification is separated using a larger data set. The input dataset is first pre-processed to remove any noise. Chi-square-based feature extraction is used during the extraction process. The Chi-Square technique is used to process the extraction areas in order to extract various features and choose the necessary features in order to improve classification. The KNN and MLP classifiers are employed for determine the precise classification. The output of the proposed technique is implemented by using the Python software. As a result this approach achieves good accuracy but takes large training times in packet level due to large amounts of data and unbalanced data.},
        keywords = {},
        month = {},
        }