AdaBoost改进的规划识别方法在入侵检测中的研究OACSTPCD
Research on AdaBoost Improved Planning Identification Method in Intrusion Detection
论文将集成学习的思想引入规划识别中,介绍了一种基于AdaBoost改进的规划识别算法并应用于入侵检测.该算法把串行的集成算法AdaBoost与传统的规划识别算法相结合,把各个规划识别模型视为弱预测器,结合AdaBoost算法把各弱预测器组合成一个强预测器.最终输出强预测器的识别结果.论文使用NSL-KDD数据集进行实验验证.实验结果对比表明,论文提出的方法相比传统方法有着更好的识别效果.
The idea of ensemble learning is introduced into planning recognition,and an improved planning recognition meth-od based on AdaBoost is proposed and applied to intrusion detection.This method combines the serial integration algorithm Ada-Boost with the traditional planning recognition algorithm,regards each planning recognition prediction model as a weak classifier,and combines each weak predictor with AdaBoost algorithm to form a strong predictor.Fina…查看全部>>
陈磊;胡广朋
江苏科技大学计算机学院 镇江 212000江苏科技大学计算机学院 镇江 212000
数学
入侵检测规划识别集成学习AdaBoost
intrusion detectionplanning identificationintegrated learningAdaBoost
《计算机与数字工程》 2023 (10)
2384-2389,6
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