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@article{186087,
        author = {Ms. Shanthala A S and Dr. H N Prakash},
        title = {Machine Learning based Advanced method of Detection and Classification of Network Anomalies},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {224-231},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186087},
        abstract = {This is an Machine Learning based advanced method of detection and classification of network anomalies. This project helps in identifying and classifying the network traffic data pattern that involves normal and abnormal data. It is useful for network activity-based sequences, as in recent times we do lot of work that are related to network-based interactions to get the information regarding useful resources. The user should know about privacy security measures including security related knowledge to get the right results in the right platform resources. By one click we can lose the data of all our important and confidential details, so network security plays an important role for these considerations. Here we have collected the publicly available datasets that has supervised and unsupervised learning model. To train and test the data we have used three various classifiers that are based on machine learning models: Support Vector Machines (SVM), Isolation Forest and K- nearest neighbour to get the best out of the approach. For frontend development we have used visualization tools such as HTML, CSS and Java script for smooth transition. For backend we have used Python, Node.js and Fast API’s and MongoDB to store the dataset files that has network-based csv files for comparison between models.},
        keywords = {Machine Learning, SVM, network traffic data, detection and classification, normal or anomalous data, React.js, Python.},
        month = {October},
        }