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On the Performance of Generative Adversarial Network by Limiting Mode Collapse for Malware Detection Systems
Generative adversarial network (GAN) has been regarded as a promising solution to many machine learning problems, and it comprises of a generator and discriminator, determining patterns and anomalies in the input data. However, GANs have several common failure modes. Typically, a mode collapse occur...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749644/ https://www.ncbi.nlm.nih.gov/pubmed/35009810 http://dx.doi.org/10.3390/s22010264 |
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author | Murray, Acklyn Rawat, Danda B. |
author_facet | Murray, Acklyn Rawat, Danda B. |
author_sort | Murray, Acklyn |
collection | PubMed |
description | Generative adversarial network (GAN) has been regarded as a promising solution to many machine learning problems, and it comprises of a generator and discriminator, determining patterns and anomalies in the input data. However, GANs have several common failure modes. Typically, a mode collapse occurs when a GAN fails to fit the set optimizations and leads to several instabilities in the generative model, diminishing the capability to generate new content regardless of the dataset. In this paper, we study conditional limiter solutions for mode collapse for the Intrusion Detection System (IDS) Control Flow GAN (ICF-GAN) model. Specifically, the ICF-GAN’s mode collapse instances are limited by a mini-batch method that significantly improves the model accuracy. Performance evaluation is conducted using numerical results obtained from experiments. |
format | Online Article Text |
id | pubmed-8749644 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87496442022-01-12 On the Performance of Generative Adversarial Network by Limiting Mode Collapse for Malware Detection Systems Murray, Acklyn Rawat, Danda B. Sensors (Basel) Article Generative adversarial network (GAN) has been regarded as a promising solution to many machine learning problems, and it comprises of a generator and discriminator, determining patterns and anomalies in the input data. However, GANs have several common failure modes. Typically, a mode collapse occurs when a GAN fails to fit the set optimizations and leads to several instabilities in the generative model, diminishing the capability to generate new content regardless of the dataset. In this paper, we study conditional limiter solutions for mode collapse for the Intrusion Detection System (IDS) Control Flow GAN (ICF-GAN) model. Specifically, the ICF-GAN’s mode collapse instances are limited by a mini-batch method that significantly improves the model accuracy. Performance evaluation is conducted using numerical results obtained from experiments. MDPI 2021-12-30 /pmc/articles/PMC8749644/ /pubmed/35009810 http://dx.doi.org/10.3390/s22010264 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Murray, Acklyn Rawat, Danda B. On the Performance of Generative Adversarial Network by Limiting Mode Collapse for Malware Detection Systems |
title | On the Performance of Generative Adversarial Network by Limiting Mode Collapse for Malware Detection Systems |
title_full | On the Performance of Generative Adversarial Network by Limiting Mode Collapse for Malware Detection Systems |
title_fullStr | On the Performance of Generative Adversarial Network by Limiting Mode Collapse for Malware Detection Systems |
title_full_unstemmed | On the Performance of Generative Adversarial Network by Limiting Mode Collapse for Malware Detection Systems |
title_short | On the Performance of Generative Adversarial Network by Limiting Mode Collapse for Malware Detection Systems |
title_sort | on the performance of generative adversarial network by limiting mode collapse for malware detection systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749644/ https://www.ncbi.nlm.nih.gov/pubmed/35009810 http://dx.doi.org/10.3390/s22010264 |
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