Open AccessResearch on Intrusion Detection Method Based on Pearson Correlation Coefficient Feature Selection Algorithm
Author(s) -
Pengtian Chen,
Fēi Li,
Chunwang Wu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012054
Subject(s) - intrusion detection system , feature selection , computer science , data mining , network security , correlation coefficient , feature (linguistics) , curse of dimensionality , selection (genetic algorithm) , pearson product moment correlation coefficient , intrusion , data security , pattern recognition (psychology) , algorithm , artificial intelligence , machine learning , computer security , statistics , mathematics , encryption , linguistics , philosophy , geochemistry , geology
The current era is the era of big data and 5G. The network security data in the network is different from the past, and the network security data is growing exponentially. As an important line of defense for network security, intrusion detection technology can efficiently detect and process massive amounts of security data has become an important factor restricting its development. The feature selection method of intrusion detection data directly affects the efficiency of intrusion detection. Therefore, this paper proposes a feature selection algorithm based on pearson correlation coefficient, which performs feature specification on many features, which greatly reduces the amount of security data that needs to be processed, and effectively reduces the dimensionality of the data to increase the intrusion. Detection efficiency.