Welcome to the InfoSci Platform
Reference Hub9
False Alert Detection Based on Deep Learning and Machine Learning

False Alert Detection Based on Deep Learning and Machine Learning

Shudong Li, Danyi Qin, Xiaobo Wu, Juan Li, Baohui Li, Weihong Han
Copyright: © 2022 |Volume: 18 |Issue: 1 |Pages: 21
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781799893967|DOI: 10.4018/IJSWIS.297035
Cite Article Cite Article

MLA

Li, Shudong, et al. "False Alert Detection Based on Deep Learning and Machine Learning." IJSWIS vol.18, no.1 2022: pp.1-21. http://doi.org/10.4018/IJSWIS.297035

APA

Li, S., Qin, D., Wu, X., Li, J., Li, B., & Han, W. (2022). False Alert Detection Based on Deep Learning and Machine Learning. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-21. http://doi.org/10.4018/IJSWIS.297035

Chicago

Li, Shudong, et al. "False Alert Detection Based on Deep Learning and Machine Learning," International Journal on Semantic Web and Information Systems (IJSWIS) 18, no.1: 1-21. http://doi.org/10.4018/IJSWIS.297035

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Among the large number of network attack alerts generated every day, actual security incidents are usually overwhelmed by a large number of redundant alerts. Therefore, how to remove these redundant alerts in real time and improve the quality of alerts is an urgent problem to be solved in large-scale network security protection. This paper uses the method of combining machine learning and deep learning to improve the effect of false alarm detection and then more accurately identify real alarms, that is, in the process of training the model, the features of a hidden layer output of the DNN model are used as input to train the machine learning model. In order to verify the proposed method, we use the marked alert data to do classification experiments, and finally use the accuracy recall rate, precision, and F1 value to evaluate the model. Good results have been obtained.