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Deep learning based hybrid analysis of malware detection and classification: A recent review

Globally extensive digital revolutions involved with every process related to human progress can easily create the critical issues in security aspects. This is promoted due to the important factors like financial crises and geographical connectivity in worse condition of the nations. By this fact,...

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Main Authors: Hussain, Syed Shuja, Mohd Faizal, Ab Razak, Ahmad Firdaus, Zainal Abidin
Format: Article
Language:English
English
Published: River Publishers 2023
Subjects:
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/39304/2/Deep%20learning%20based%20hybrid%20analysis%20of%20malware%20detection%20and%20classification.pdf
http://umpir.ump.edu.my/id/eprint/39304/9/Deep%20Learning%20Based%20Hybrid%20Analysis%20of%20Malware%20Detection%20and%20Classification.pdf
http://umpir.ump.edu.my/id/eprint/39304/
https://doi.org/10.13052/jcsm2245-1439.1314
https://doi.org/10.13052/jcsm2245-1439.1314
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spelling my.ump.umpir.393042023-12-28T08:31:15Z http://umpir.ump.edu.my/id/eprint/39304/ Deep learning based hybrid analysis of malware detection and classification: A recent review Hussain, Syed Shuja Mohd Faizal, Ab Razak Ahmad Firdaus, Zainal Abidin QA76 Computer software Globally extensive digital revolutions involved with every process related to human progress can easily create the critical issues in security aspects. This is promoted due to the important factors like financial crises and geographical connectivity in worse condition of the nations. By this fact, the authors are well motivated to present a precise literature on malware detection with deep learning approach. In this literature, the basic overview includes the nature of nature of malware detection i.e., static, dynamic, and hybrid approach. Another major component of this articles is the investigation of the backgrounds from recently published and highly cited state-of-the-arts on malware detection, prevention and prediction with deep learning frameworks. The technologies engaged in providing solutions are utilized from AI based frameworks like machine learning, deep learning, and hybrid frameworks. The main motivations to produce this article is to portrait clear pictures of the option challenging issues and corresponding solution for developing robust malware-free devices. In the lack of a robust malware-free devices, highly growing geographical and financial disputes at wide globes can be extensively provoked by malicious groups. Therefore, exceptionally high demand of the malware detection devices requires a very strong recommendation to ensure the security of a nation. In terms preventing and recovery, Zero-day threats can be handled by recent methodology used in deep learning. In the conclusion, we also explored and investigated the future patterns of malware and how deals with in upcoming years. Such review may extend towards the development of IoT based applications used many fields such as medical devices, home appliances, academic systems. River Publishers 2023 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/39304/2/Deep%20learning%20based%20hybrid%20analysis%20of%20malware%20detection%20and%20classification.pdf pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/39304/9/Deep%20Learning%20Based%20Hybrid%20Analysis%20of%20Malware%20Detection%20and%20Classification.pdf Hussain, Syed Shuja and Mohd Faizal, Ab Razak and Ahmad Firdaus, Zainal Abidin (2023) Deep learning based hybrid analysis of malware detection and classification: A recent review. Journal of Cyber Security and Mobility, 13 (1). pp. 91-134. ISSN 2245-4578. (Published) https://doi.org/10.13052/jcsm2245-1439.1314 https://doi.org/10.13052/jcsm2245-1439.1314
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA76 Computer software
spellingShingle QA76 Computer software
Hussain, Syed Shuja
Mohd Faizal, Ab Razak
Ahmad Firdaus, Zainal Abidin
Deep learning based hybrid analysis of malware detection and classification: A recent review
description Globally extensive digital revolutions involved with every process related to human progress can easily create the critical issues in security aspects. This is promoted due to the important factors like financial crises and geographical connectivity in worse condition of the nations. By this fact, the authors are well motivated to present a precise literature on malware detection with deep learning approach. In this literature, the basic overview includes the nature of nature of malware detection i.e., static, dynamic, and hybrid approach. Another major component of this articles is the investigation of the backgrounds from recently published and highly cited state-of-the-arts on malware detection, prevention and prediction with deep learning frameworks. The technologies engaged in providing solutions are utilized from AI based frameworks like machine learning, deep learning, and hybrid frameworks. The main motivations to produce this article is to portrait clear pictures of the option challenging issues and corresponding solution for developing robust malware-free devices. In the lack of a robust malware-free devices, highly growing geographical and financial disputes at wide globes can be extensively provoked by malicious groups. Therefore, exceptionally high demand of the malware detection devices requires a very strong recommendation to ensure the security of a nation. In terms preventing and recovery, Zero-day threats can be handled by recent methodology used in deep learning. In the conclusion, we also explored and investigated the future patterns of malware and how deals with in upcoming years. Such review may extend towards the development of IoT based applications used many fields such as medical devices, home appliances, academic systems.
format Article
author Hussain, Syed Shuja
Mohd Faizal, Ab Razak
Ahmad Firdaus, Zainal Abidin
author_facet Hussain, Syed Shuja
Mohd Faizal, Ab Razak
Ahmad Firdaus, Zainal Abidin
author_sort Hussain, Syed Shuja
title Deep learning based hybrid analysis of malware detection and classification: A recent review
title_short Deep learning based hybrid analysis of malware detection and classification: A recent review
title_full Deep learning based hybrid analysis of malware detection and classification: A recent review
title_fullStr Deep learning based hybrid analysis of malware detection and classification: A recent review
title_full_unstemmed Deep learning based hybrid analysis of malware detection and classification: A recent review
title_sort deep learning based hybrid analysis of malware detection and classification: a recent review
publisher River Publishers
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/39304/2/Deep%20learning%20based%20hybrid%20analysis%20of%20malware%20detection%20and%20classification.pdf
http://umpir.ump.edu.my/id/eprint/39304/9/Deep%20Learning%20Based%20Hybrid%20Analysis%20of%20Malware%20Detection%20and%20Classification.pdf
http://umpir.ump.edu.my/id/eprint/39304/
https://doi.org/10.13052/jcsm2245-1439.1314
https://doi.org/10.13052/jcsm2245-1439.1314
_version_ 1822923965192994816
score 13.232414

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