A percentile-based reward for DRL-based RAN slicing meets delay-violation probability constraints while cutting average delay by 38% versus an average-delay baseline, and a reward-weighted model personalization method outperforms federated averaging.
Machine Learning-based Early Attack Detection Using Open RAN Intelligent Controller
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We design and demonstrate a method for early detection of Denial-of-Service attacks. The proposed approach takes advantage of the OpenRAN framework to collect measurements from the air interface (for attack detection) and to dynamically control the operation of the Radio Access Network (RAN). For that purpose, we developed our near-Real Time (RT) RAN Intelligent Controller (RIC) interface. We apply and analyze a wide range of Machine Learning algorithms to data traffic analysis that satisfy the accuracy and latency requirements set by the near-RT RIC. Our results show that the proposed framework is able to correctly classify genuine vs. malicious traffic with high accuracy (i.e., 95%) in a realistic testbed environment, allowing us to detect attacks already at the Distributed Unit (DU), before malicious traffic even enters the Centralized Unit (CU).
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN
A percentile-based reward for DRL-based RAN slicing meets delay-violation probability constraints while cutting average delay by 38% versus an average-delay baseline, and a reward-weighted model personalization method outperforms federated averaging.