【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。更多还原

【Abstract】 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.更多还原