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@article{174064,
        author = {Margret Sharmila F and Sandhya Barathi K and Shanmitha M and Praveen Kumar S and Ethin Abinav V},
        title = {Enhancing Network Security with Anomaly Detection: A Machine Learning Approach Using Decision Trees, Random Forest, and SVM for Real-Time Intrusion Monitoring},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {2842-2851},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174064},
        abstract = {The goal of this project is to create an intrusion detection system (IDS) that uses machine learning to categorize network data into normal and abnormal categories for network security. The system uses decision trees, logistic regression, and support vector machines (SVM) to classify the traffic. Network traffic and data byte counts are preprocessed at the protocol level and used as model training inputs. Online network traffic anomalies, both known and unknown, can be identified by the trained models. All prediction visualization, real-time monitoring, and alerts for questionable activity are accessible on the Streamlit dashboard for the user's convenience. The system is built to evolve with the network, and anytime new traffic data is received, the model is immediately retrained to ensure consistently optimal performance. The method is scalable, effective, and reliable for addressing today's network security issues.},
        keywords = {Network security, decision trees, support vector machines, logistic regression, anomaly detection, real-time monitoring, Streamlit, machine learning, intrusion detection systems, and model retraining.},
        month = {March},
        }