MACHINE LEARNING BASED CLOUD COMPUTING INTRUSION DETECTION
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Abstract
Based on today’s technologically networked world, a sophisticated networking technology known as Software-Defined Networking (SDN) is utilized in cloud computing environments to improve the effectiveness of network management. However, SDN’s centralized nature makes it vulnerable to DDoS attacks. This study introduces a technique for detecting DDoS attacks within a cloud computing setting. The research seeks to apply an ensemble machine learning approach for statistically identifying DDoS attacks in cloud network traffic, categorizing them as either harmful or harmless. Various machine learning algorithms, including K-Nearest Neighbors, Random Forest (RF), and Decision Tree, were utilized as foundational classifiers in the suggested ensemble machine learning model. A dataset of SDN–DDoS attacks was utilized to assess the efficacy of the base classifiers. The classifiers were trained using 80% of the dataset and evaluated on 20%. The results of the experiment indicated that the Random Forest and Random Forest classifiers attained 100% accuracy, whereas the K-Nearest Neighbor classifier achieved an accuracy of 98.21%. The ensemble machine learning model employed a majority voting technique for final prediction and achieved an accuracy of 100% on the test set, ranking as the best compared to benchmark models.
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References
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