BAE: Anomaly Detection Algorithm Based on Clustering and Autoencoder

Dongqi Wang, Mingshuo Nie and Dongming Chen ()
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Dongqi Wang: Software College, Northeastern University, Shenyang 110169, China
Mingshuo Nie: Software College, Northeastern University, Shenyang 110169, China
Dongming Chen: Software College, Northeastern University, Shenyang 110169, China

Mathematics, 2023, vol. 11, issue 15, 1-14

Abstract: In this paper, we propose an outlier-detection algorithm for detecting network traffic anomalies based on a clustering algorithm and an autoencoder model. The BIRCH clustering algorithm is employed as the pre-algorithm of the autoencoder to pre-classify datasets with complex data distribution characteristics, while the autoencoder model is used to detect outliers based on a threshold. The proposed BIRCH-Autoencoder (BAE) algorithm has been tested on four network security datasets, KDDCUP99, UNSW-NB15, CICIDS2017, and NSL-KDD, and compared with representative algorithms. The BAE algorithm achieved average F-scores of 96.160, 81.132, and 91.424 on the KDDCUP99, UNSW-NB15, and CICIDS2017 datasets, respectively. These experimental results demonstrate that the proposed approach can effectively and accurately detect anomalous data.

Keywords: pre-classification; BIRCH; Autoencoder; anomaly detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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