A Comparative Study of ML-ELM and DNN for Intrusion Detection-Reference-Cited by-同舟云学术

A Comparative Study of ML-ELM and DNN for Intrusion Detection

Published:2021-02 Issue: Volume: Page:
ISSN:
Container-title:2021 Australasian Computer Science Week Multiconference
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Author:

Yang Wencheng¹,Wang Song²,Johnstone Michael¹

Affiliation:

1. Edith Cowan University, Australia

2. La Trobe University, Australia

Funder

Cyber Security Research Centre Limited

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3437378.3437390

Reference34 articles.

1. Deep-Learning-for-IDS. Retrieved Access date: 01/07/2020 from https://github.com/locnguyen21/Deep-Learning-for-IDS. Deep-Learning-for-IDS. Retrieved Access date: 01/07/2020 from https://github.com/locnguyen21/Deep-Learning-for-IDS.

2. NSL-KDD. Retrieved 01/07/2020 from https://www.unb.ca/cic/datasets/nsl.html. NSL-KDD. Retrieved 01/07/2020 from https://www.unb.ca/cic/datasets/nsl.html.

3. Source Codes of ML-ELM. Retrieved Access date: 30/09/2018 from http://www.ntu.edu.sg/home/egbhuang/elm_codes.html. Source Codes of ML-ELM. Retrieved Access date: 30/09/2018 from http://www.ntu.edu.sg/home/egbhuang/elm_codes.html.

4. Multi-Level Hybrid Support Vector Machine and Extreme Learning Machine Based on Modified K-Means for Intrusion Detection System;Al-Yaseen Wathiq Laftah;Expert Systems with Applications,2017

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