BROWSE

Related Scientist

Adhi Tama, Bayu's photo.

Adhi Tama, Bayu
데이터 사이언스 그룹
more info

ITEM VIEW & DOWNLOAD

A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection

Cited 0 time in webofscience Cited 0 time in scopus
148 Viewed 0 Downloaded
Title
A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection
Author(s)
Nkenyereye, Lewis; Bayu Adhi Tama; Lim, Sunghoon
Publication Date
2021
Journal
CMC-COMPUTERS MATERIALS & CONTINUA, v.66, no.2, pp.2217 - 2227
Publisher
TECH SCIENCE PRESS
Abstract
An anomaly-based intrusion detection system (A-IDS) provides a critical aspect in a modern computing infrastructure since new types of attacks can be discovered. It prevalently utilizes several machine learning algorithms (ML) for detecting and classifying network traffic. To date, lots of algorithms have been proposed to improve the detection performance of A-IDS, either using individual or ensemble learners. In particular, ensemble learners have shown remarkable performance over individual learners in many applications, including in cybersecurity domain. However, most existing works still suffer from unsatisfactory results due to improper ensemble design. The aim of this study is to emphasize the effectiveness of stacking ensemble-based model for A-IDS, where deep learning (e.g., deep neural network [DNN]) is used as base learner model. The effectiveness of the proposed model and base DNN model are benchmarked empirically in terms of several performance metrics, i.e., Matthew's correlation coefficient, accuracy, and false alarm rate. The results indicate that the proposed model is superior to the base DNN model as well as other existing ML algorithms found in the literature.
URI
https://pr.ibs.re.kr/handle/8788114/9079
DOI
10.32604/cmc.2020.012432
ISSN
1546-2218
Appears in Collections:
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > Data Science Group(데이터 사이언스 그룹) > 1. Journal Papers (저널논문)
Files in This Item:
There are no files associated with this item.

qrcode

  • facebook

    twitter

  • Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse