Research on an Intrusion Detection Algorithm Based on PCA and Random-forest Classification

doi:10.3969/j.issn.1671-1122.2017.11.008

Abstract

Abstract:

Due to the low accuracy of existing intrusion detection methods, this paper proposes an intrusion detection algorithm based on PCA (principle component analysis) and random-forest classification. The idea of the algorithm is to clean the training data before classifying. Firstly, PCA is used to decompose the dataset and reduce noises. Then random-forest classifier is used to classify and train the processed data. The experiment uses machine learning library based on Python called scikit-learn and 20% NSL-KDD dataset. Experimental results show that compared with the commonly used intrusion detection technologies based on machine learning, the intrusion detection algorithm proposed in this paper can improve the detection accuracy more effectively.

Key words: machine learning, intrusion detection, PCA, random-forest

CLC Number:

TP309

Weining LIN, Mingzhi CHEN, Yunqing ZHAN, Chuanbao LIU. Research on an Intrusion Detection Algorithm Based on PCA and Random-forest Classification[J]. Netinfo Security, 2017, 17(11): 50-54.

Figures/Tables 4

References 18

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