首页|Study Data from Huaqiao University Provide New Insights into Machine Learning (S ampling-Based Machine Learning Models for Intrusion Detection in Imbalanced Data set)
Study Data from Huaqiao University Provide New Insights into Machine Learning (S ampling-Based Machine Learning Models for Intrusion Detection in Imbalanced Data set)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news reporting originating from X iamen, People's Republic of China, by NewsRx correspondents, research stated, "C ybersecurity is one of the important considerations when adopting IoT devices in smart applications." Our news reporters obtained a quote from the research from Huaqiao University: " Even though a huge volume of data is available, data related to attacks are gene rally in a significantly smaller proportion. Although machine learning models ha ve been successfully applied for detecting security attacks on smart application s, their performance is affected by the problem of such data imbalance. In this case, the prediction model is preferable to the majority class, while the perfor mance for predicting the minority class is poor. To address such problems, we ap ply two oversampling techniques and two undersampling techniques to balance the data in different categories. To verify their performance, five machine learning models, namely the decision tree, multi-layer perception, random forest, XGBoos t, and CatBoost, are used in the experiments based on the grid search with 10-fo ld cross-validation for parameter tuning. The results show that both the oversam pling and undersampling techniques can improve the performance of the prediction models used."
Huaqiao UniversityXiamenPeople's Rep ublic of ChinaAsiaCybersecurityCyborgsEmerging TechnologiesMachine Lea rning
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