A Deep Reinforcement Learning Framework to Evade Black-Box Machine Learning Based IoT Malware Detectors Using GAN-Generated Influential Features-Reference-Cited by-同舟云学术

A Deep Reinforcement Learning Framework to Evade Black-Box Machine Learning Based IoT Malware Detectors Using GAN-Generated Influential Features

Published:2023 Issue: Volume:11 Page:133717-133729
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Arif Rahat Maqsood¹^ORCID,Aslam Muhammad¹,Al-Otaibi Shaha²^ORCID,Martinez-Enriquez Ana Maria³,Saba Tanzila⁴,Bahaj Saeed Ali⁵^ORCID,Rehman Amjad⁴^ORCID

Affiliation:

1. Department of Computer Science, University of Engineering and Technology, Lahore, Lahore, Pakistan

2. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

3. Department of CS, CINVESTAV-IPN, Gustavo A. Madero, Mexico

4. Artificial Intelligence and Data Analytics (AIDA) Laboratory, CCIS, Prince Sultan University, Riyadh, Saudi Arabia

5. MIS Department, College of Business Administration, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia

Funder

Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia, through Princess Nourah bint Abdulrahman University Researchers Supporting Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10322868.pdf?arnumber=10322868

Reference52 articles.

1. IoT security: Review, blockchain solutions, and open challenges

2. A Lightweight Blockchain-Based Remote Mutual Authentication for AI-Empowered IoT Sustainable Computing Systems

3. McAfee labs threats report: June 2021;Samani,2021

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