A Robust Framework for MADS Based on DL Techniques on the IoT

Day after day, new types of malware are appearing, renewing, and continuously developing, which makes it difficult to identify and stop them. Some attackers exploit artificial intelligence (AI) to create renewable malware with different signatures that are difficult to detect. Therefore, the perform...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Hussah Talal, Rachid Zagrouba
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	anomaly detection system deep learning techniques IoT malware detection LSTM autoencoder Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/8d91ad0d89bd4fb7b5e751697514d8e4
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:8d91ad0d89bd4fb7b5e751697514d8e4
record_format	dspace
spelling	oai:doaj.org-article:8d91ad0d89bd4fb7b5e751697514d8e42021-11-11T15:42:47ZA Robust Framework for MADS Based on DL Techniques on the IoT10.3390/electronics102127232079-9292https://doaj.org/article/8d91ad0d89bd4fb7b5e751697514d8e42021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2723https://doaj.org/toc/2079-9292Day after day, new types of malware are appearing, renewing, and continuously developing, which makes it difficult to identify and stop them. Some attackers exploit artificial intelligence (AI) to create renewable malware with different signatures that are difficult to detect. Therefore, the performance of the traditional malware detection systems (MDS) and protection mechanisms were weakened so the malware can easily penetrate them. This poses a great risk to security in the internet of things (IoT) environment, which is interconnected and has big and continuous data. Penetrating any of the things in the IoT environment leads to a penetration of the entire IoT network and control different devices on it. Also, the penetration of the IoT environment leads to a violation of users’ privacy, and this may result in many risks, such as obtaining and stealing the user’s credit card information or theft of identity. Therefore, it is necessary to propose a robust framework for a MDS based on DL that has a high ability to detect renewable malware and propose malware Anomaly detection systems (MADS) work as a human mind to solve the problem of security in IoT environments. RoMADS model achieves high results: 99.038% for <i>Accuracy</i>, 99.997% for Detection rate. The experiment results overcome eighteen models of the previous research works related to this field, which proved the effectiveness of RoMADS framework for detecting malware in IoT.Hussah TalalRachid ZagroubaMDPI AGarticleanomaly detection systemdeep learning techniquesIoTmalware detectionLSTM autoencoderElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2723, p 2723 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	anomaly detection system deep learning techniques IoT malware detection LSTM autoencoder Electronics TK7800-8360
spellingShingle	anomaly detection system deep learning techniques IoT malware detection LSTM autoencoder Electronics TK7800-8360 Hussah Talal Rachid Zagrouba A Robust Framework for MADS Based on DL Techniques on the IoT
description	Day after day, new types of malware are appearing, renewing, and continuously developing, which makes it difficult to identify and stop them. Some attackers exploit artificial intelligence (AI) to create renewable malware with different signatures that are difficult to detect. Therefore, the performance of the traditional malware detection systems (MDS) and protection mechanisms were weakened so the malware can easily penetrate them. This poses a great risk to security in the internet of things (IoT) environment, which is interconnected and has big and continuous data. Penetrating any of the things in the IoT environment leads to a penetration of the entire IoT network and control different devices on it. Also, the penetration of the IoT environment leads to a violation of users’ privacy, and this may result in many risks, such as obtaining and stealing the user’s credit card information or theft of identity. Therefore, it is necessary to propose a robust framework for a MDS based on DL that has a high ability to detect renewable malware and propose malware Anomaly detection systems (MADS) work as a human mind to solve the problem of security in IoT environments. RoMADS model achieves high results: 99.038% for <i>Accuracy</i>, 99.997% for Detection rate. The experiment results overcome eighteen models of the previous research works related to this field, which proved the effectiveness of RoMADS framework for detecting malware in IoT.
format	article
author	Hussah Talal Rachid Zagrouba
author_facet	Hussah Talal Rachid Zagrouba
author_sort	Hussah Talal
title	A Robust Framework for MADS Based on DL Techniques on the IoT
title_short	A Robust Framework for MADS Based on DL Techniques on the IoT
title_full	A Robust Framework for MADS Based on DL Techniques on the IoT
title_fullStr	A Robust Framework for MADS Based on DL Techniques on the IoT
title_full_unstemmed	A Robust Framework for MADS Based on DL Techniques on the IoT
title_sort	robust framework for mads based on dl techniques on the iot
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8d91ad0d89bd4fb7b5e751697514d8e4
work_keys_str_mv	AT hussahtalal arobustframeworkformadsbasedondltechniquesontheiot AT rachidzagrouba arobustframeworkformadsbasedondltechniquesontheiot AT hussahtalal robustframeworkformadsbasedondltechniquesontheiot AT rachidzagrouba robustframeworkformadsbasedondltechniquesontheiot
_version_	1718434097749032960

A Robust Framework for MADS Based on DL Techniques on the IoT

Ejemplares similares