An Adaptive Ensemble Machine Learning Model for Intrusion Detection

By Xianwei Gao, Chun Shan, Changzhen Hu, Zequn Niu, Zhen Liu. IEEE Access, 2019

410 citations recorded by the source corpus.

In recent years, advanced threat attacks are increasing, but the traditional network intrusion detection system based on feature filtering has some drawbacks which make it difficult to find new attacks in time. This paper takes NSL-KDD data set as the research object, analyses the latest progress and existing problems in the field of intrusion detection technology, and proposes an adaptive ensemble learning model. By adjusting the proportion of training data and setting up multiple decision trees, we construct a MultiTree algorithm. In order to improve the overall detection effect, we choose several base classifiers, including decision tree, random forest, kNN, DNN, and design an ensemble adaptive voting algorithm. We use NSL-KDD Test+ to verify our approach, the accuracy of the MultiTree algorithm is 84.2%, while the final accuracy of the adaptive voting algorithm reaches 85.2%. Compared with other research papers, it is proved that our ensemble model effectively improves detection accuracy. In addition, through the analysis of data, it is found that the quality of data features is an important factor to determine the detection effect. In the future, we should optimize the feature selection and preprocessing of intrusion detection data to achieve better results.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/85a184fb63ad75a06df858094c0de20a39670128

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In Artificial Intelligence

Citations in the corpus

A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU (2020, background): “Various researchers have worked to resolve such performance issues by implementing various machine learning approaches on IDS datasets [6].”
Network intrusion detection system: A systematic study of machine learning and deep learning approaches (2020, methodology): “In the next section, we Yao et al 72 Vinayakumar et al 116 Gao et al 82 Wei N a s ee r e t a l 94 C o m p a r i s o n o f M L - a n d D L - b a s e d N I D S a l g o r i t h m s b y i m p l e m e n t i n g o n G P U - i n t e g r a t e d t e s t b e d .”