Applying three explainable AI tools to a random forest model on the WUSTL-HDRL-2024 dataset yields qualitative feature-level explanations aimed at attack detection in 6G healthcare networks, but the validation is limited to a single sample.
HDRL -IDS: A Hybrid Deep Reinforcement Learning Intrusion Detection System for Enhancing the Security of Medical Applications in 5G Networks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Explainable AI for Securing Healthcare in IoT-Integrated 6G Wireless Networks
Applying three explainable AI tools to a random forest model on the WUSTL-HDRL-2024 dataset yields qualitative feature-level explanations aimed at attack detection in 6G healthcare networks, but the validation is limited to a single sample.