Intelligent Pattern Recognition Using Equilibrium Optimizer With Deep Learning Model for Android Malware Detection-Reference-Cited by-同舟云学术

Intelligent Pattern Recognition Using Equilibrium Optimizer With Deep Learning Model for Android Malware Detection

Published:2024 Issue: Volume:12 Page:24516-24524
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Maray Mohammed¹^ORCID,Maashi Mashael²^ORCID,Alshahrani Haya Mesfer³,Aljameel Sumayh S.⁴^ORCID,Abdelbagi Sitelbanat⁵^ORCID,Salama Ahmed S.⁶^ORCID

Affiliation:

1. Department of Information Systems, College of Computer Science, King Khalid University, Abha, Saudi Arabia

2. Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 103786, Riyadh, Saudi Arabia

3. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia

4. Department of Computer Science, Saudi Aramco Cybersecurity Chair, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

5. Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia

6. Department of Electrical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo, Egypt

Funder

Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project

Princess Nourah bint Abdulrahman University Researchers Supporting Project number

Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Research

King Saud University, Riyadh, Saudi Arabia

Prince Sattam bin Abdulaziz University project number

Future University in Egypt

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10413469.pdf?arnumber=10413469

Reference22 articles.

1. Deep Learning-Based Attack Detection and Classification in Android Devices

2. On the Impact of Sample Duplication in Machine-Learning-Based Android Malware Detection

3. You are what the permissions told me! Android malware detection based on hybrid tactics

4. Adversarial superiority in android malware detection: Lessons from reinforcement learning based evasion attacks and defenses

5. De-LADY: Deep learning-based Android malware detection using Dynamic features;Sihag;J. Internet Serv. Inf. Secur.,2021

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