基于机器学习的端口扫描入侵检测  

Machine Learning-based Port Scanning Intrusion Detection

作　　者：郭楚栩 施勇[1] 薛质[1] GUO Chu-xu;SHI Yong;XUE Zhi(School of Electronic Information and Electrical Engineering,Shanghai Jiaotong University,Shanghai 200240,China)

机构地区：[1]上海交通大学电子信息与电气工程学院

出　　处：《通信技术》2020年第2期421-426,共6页Communications Technology

基　　金：国家重点研发计划项目“网络空间安全”重点专项（No.2017YFB0803200）~~

摘　　要：在入侵检测系统发展的30年间,不断有新的检测方法被提出。在如今的第四次工业革命——人工智能的潮流中,机器学习算法为各种系统的方法解决提供了新的思路。基于2018年Daniel Fraunholz等人提出了的入侵检测模型,提出了一种基于机器学习的端口扫描检测系统,其中系统的特征提取参考了KDD Cup 99数据集中数据的特征提取,而其中的模型训练集是基于CICIDS2017数据集的。最后,模型测试结果优良。In the 30 years of the development of intrusion detection systems,new detection methods are continuously proposed.In today’s fourth industrial revolution,that is,the tide of artificial intelligence,machine learning algorithms provide new ideas for the solution of various systems.Based on the intrusion detection model proposed by Daniel Fraunholz et al.in 2018,a machine learning-based port scanning detection system is proposed,in which the feature extraction refers to the feature extraction of the data in the KDD Cup 99 dataset,and the model training set is based on the CICIDS2017 dataset.Finally,the model test results are fairly good.

关 键 词：端口扫描 入侵检测系统 特征提取 机器学习 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术] TP181[自动化与计算机技术—计算机科学与技术]