点击次数：
第一作者：Dongqi Wang
合写作者：Mingshuo Nie,Dongming Chen
发表刊物：Mathematics
期号：15
卷号：11
摘要：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.
关键字：Pre-classification; BIRCH; Autoencoder; anomaly detection
论文类型：SCI JCR Q1
学科门类：工学
文献类型：JCR 一区
一级学科：计算机科学与技术
页面范围：3398-3411
是否译文：否