基于机器学习的网络流量特征选择

互联网技术水平不断提高的同时,也带来了日益复杂的网络安全问题,传统地利用端口检测和深度包检测等流量识别技术已经难以应对当下日趋复杂的网络环境.伴随着机器学习理论的成熟,机器学习方法已经成功的应用于图像识别、声音辨别、医疗等各个领域,机器学习使用计算机模拟人类的活动,通过学习现有的知识,建立有效的学习模型,进一步对未知的数据进行预测或者分类.将机器学习方法应用在网络流量识别领域,首先对网络流量识别的研究现状和机器学习作了相关的介绍,其次基于3种机器学习分类算法,对比分析了不同特征选择算法对网络流量识别准确率的影响,提出了改进的特征选择算法,并经过实验验证了改进后特征选择算法的有效性.

Research of network flow feature selection based on machine learning

With the development of Internet technology,it also brings an increasingly complex network security issues.The traditional traffic recognition technology such as port detection and deep packet inspection has been difficult to deal with the current increasingly complex network environment.As the theory of machine learning become mature,it has been successfully applied in many subject areas such as Image or voice recogmtion and medical fields.Machine learning methods simulate the human cognitive pattern by computers.The target of machine learning is to establish learning model by studying the existing knowledge and use the learning model to class or predict unknown data.In this research,machine learning methods were applied in internet traffic identification.Firstly,we introduced the research status of internet traffic identification and the relevant concepts of machine learning.Secondly,the main work is to research and compare the influence of different feature selection to identification accuracy based on three machine learning classification algorithms.The author proposed an improved feature selection algorithm and verified the effectiveness of this algorithm by experiments.