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A State-of-the-Art Survey of Adversarial Reinforcement Learning for IoT Intrusion Detection

Year: 2025

Type: dissertation

Abstract: Adversarial Reinforcement Learning (ARL) models for intelligent devices and Network Intrusion Detection Systems (NIDS) improve system resilience against sophisticated cyber-attacks. As a core component of ARL, Adversarial Tra... | Find, read and cite all the research you need on Tech Science Press

Source: Computers, materials & continua/Computers, materials & continua (Print)

DOI: 10.32604/cmc.2025.073540

Authors: Qasem Abu Al-Haija, Shahad Al Tamimi

Institutions: Jordan University of Science and Technology, Princess Sumaya University for Technology

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W7117292102

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Cited by: 3

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