Open AccessA hybrid feature selection algorithm combining information gain and genetic search for intrusion detection
Author(s) -
Yingshang Liu,
Sheng Liang,
Wenchong Fang,
Zhifeng Zhou,
Rong Hu,
Huafeng Zhou,
Jianrong Hou,
Yichang Wang
Publication year - 2020
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 - 10.1088/1742-6596/1601/3/032048
Subject(s) - computer science , intrusion detection system , fitness function , genetic algorithm , feature selection , data mining , information gain , feature (linguistics) , selection (genetic algorithm) , information gain ratio , process (computing) , algorithm , pattern recognition (psychology) , artificial intelligence , machine learning , linguistics , philosophy , operating system
Network attacks are one of the main threats to the stable operation of smart grid equipment. As a real-time monitoring system to prevent network attacks, intrusion detection is widely used in smart grid protection. However, the massive data in the network transmission process contains a large number of redundant and irrelevant features, which makes it difficult for the intrusion detection system to process in time and reduce the efficiency. Feature selection is a method to solve this kind of problem. It can improve the speed of intrusion detection by filtering the characteristics of massive data. Therefore, a hybrid feature selection algorithm which combines information gain and genetic search to improve the work efficiency of intrusion detection systems is proposed. The algorithm is mainly divided into three parts. Firstly, the information gain value of all features is calculated by using information gain, according to which all features are ordered, and the ordered features is ranked according to an exponential increase strategy; secondly, the ranked features is used to guide the genetic algorithm search process, and a new fitness function can be used to control the search direction of genetic algorithm; finally, a classification algorithm is used to test the dataset after feature selection. In experiments, by comparing with other feature selection algorithms on 5 sets of high-dimensional UCI datasets, it is concluded that the IG Exp GA proposed in this paper significantly improves the detection rate and detection speed. More importantly, in the KDD1998 network data, the algorithm proposed improves the detection rate to 98.8%, which is significantly better than other algorithms.