摘要
基于支持向量机的入侵检测模型检测效率较低,为此,提出一种基于图形处理器(GPU)和特征选择的入侵检测模型。在入侵检测过程中,采用基于GPU的并行计算模型进行训练,并对样本的特征进行合理选择,从而提高检测效率。实验结果表明,在保证系统性能的情况下,该模型可以缩短训练时间。
In order to optimize test efficiency of Intrusion Detection System（IDS） based on Support Vector Machine（SVM）, a new intrusion detection method based on Graphics Processing Unit（GPU） and feature selection is proposed. During the process of intrusion detection, GPU-based parallel computing model is adopted and features of samples are reasonable selected. Experimental results demonstrate that the proposed method can reduce time consumption in the training procedure of IDS and the performance for intrusion detection is kept as usual.
出处
《计算机工程》
CAS
CSCD
2012年第8期111-113,116,共4页
Computer Engineering
基金
上海市教委科研创新基金资助项目(09YZ370)
上海工程技术大学科技发展基金资助项目(2011XY16)
关键词
入侵检测
支持向量机
图形处理器
统一计算设备架构
特征选择
并行计算
intrusion detection
Support Vector Machine（SVM）
Graphics Processing Unit（GPU）
Compute Unified Device Architecmre（CUDA）
feature selection
parallel computing
