Deep Neural Networks for Enhanced Security: Detecting Metamorphic Malware in IoT Devices-Reference-Cited by-同舟云学术

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Deep Neural Networks for Enhanced Security: Detecting Metamorphic Malware in IoT Devices

Published:2024 Issue: Volume:12 Page:48570-48582
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Habib Faiza¹^ORCID,Shirazi Syed Hamad²^ORCID,Aurangzeb Khursheed³^ORCID,Khan Asfandyar²^ORCID,Bhushan Bharat⁴,Alhussein Musaed³^ORCID

Affiliation:

1. Department of Computer Science, Abasyn University, Islamabad Campus, Islamabad, Pakistan

2. Department of Computer Science, Hazara University Mansehra, Mansehra, Pakistan

3. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

4. Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, India

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10487938.pdf?arnumber=10487938

Reference36 articles.

1. An Empirical Study on System Level Aspects of Internet of Things (IoT)

2. Learning impact of recent ICT advances based on virtual reality IoT sensors in a metaverse environment

3. IoT-Enabled Smart Applications and Challenges

4. Challenges and Issues of ICT in Industry 4.0

5. QoS-Aware Cost Minimization Strategy for AMI Applications in Smart Grid Using Cloud Computing

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