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Enhancing intrusion detection with feature selection and neural network
Author(s) -
Wu Chunhui,
Li Wenjuan
Publication year - 2021
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22397
Subject(s) - computer science , intrusion detection system , feature selection , artificial neural network , anomaly detection , machine learning , data mining , artificial intelligence , set (abstract data type) , selection (genetic algorithm) , random forest , feature (linguistics) , anomaly based intrusion detection system , linguistics , philosophy , programming language
Intrusion detection systems are widely implemented to protect computer networks from threats. To identify unknown attacks, many machine learning algorithms like neural networks have been explored for anomaly based detection. However, in real‐world applications, the performance of classifiers might be fluctuant with different data sets, while one main reason is due to some redundant or ineffective features. To mitigate this issue, this study investigates some feature selection methods and introduces an ensemble of Neural Networks and Random Forest to improve the detection performance. In particular, we design an intelligent system that can choose an appropriate algorithm in an adaptive way. In the evaluation, we study the feasibility of our approach with KDD99 data set and evaluate its practical performance with a real data set collected from a Honeynet environment. The experimental results indicate that as compared with similar approaches, our approach can overall provide a better result, through identifying important and closely related features.