Publication Type : Conference Paper
Publisher : IEEE
Source : 2024 IEEE 9th International Conference for Convergence in Technology (I2CT)
Url : https://doi.org/10.1109/i2ct61223.2024.10544217
Campus : Bengaluru
School : School of Computing
Department : Computer Science and Engineering
Year : 2024
Abstract : In the rapidly evolving digital landscape network security and efficiency are important especially with the increasingly widespread network anomalies that can cause severe performance degradation. Using dataset, Data preprocessing is performed. which includes removing missing values and standardizing attributes. A comparative analysis is been done among the models to determine the finest among them. The algorithm taken for comparing is Isolation Forest and it is compared with Random Forest, Decision Tree, K-mean and Logistic Regression. And the metrics for the comparison are accuracy, precision, recall, F1 score, training time and testing time.
Cite this Research Publication : Siddhesh T.S., Shinu M. Rajagopal, Sreebha Bhaskaran, Comparative Analysis of Machine Learning Algorithms for Anomaly Detection, 2024 IEEE 9th International Conference for Convergence in Technology (I2CT), IEEE, 2024, https://doi.org/10.1109/i2ct61223.2024.10544217