Enhancing Network Security with Anomaly Detection: A Machine Learning Approach Using Decision Trees, Random Forest, and SVM for Real-Time Intrusion Monitoring

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Margret Sharmila F; Sandhya Barathi K; Shanmitha M; Praveen Kumar S; Ethin Abinav V

Enhancing Network Security with Anomaly Detection: A Machine Learning Approach Using Decision Trees, Random Forest, and SVM for Real-Time Intrusion Monitoring

Authors: Margret Sharmila F, Sandhya Barathi K, Shanmitha M, Praveen Kumar S, Ethin Abinav V

Unique Paper ID: 174064
Volume: 11
Issue: 10
PageNo: 2842-2851

Keywords: Network security decision trees support vector machines logistic regression anomaly detection real-time monitoring Streamlit machine learning intrusion detection systems and model retraining.

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.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 10
PageNo: 2842-2851

Enhancing Network Security with Anomaly Detection: A Machine Learning Approach Using Decision Trees, Random Forest, and SVM for Real-Time Intrusion Monitoring

Available:https://ijirt.org/article?manuscript=174064

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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