DeepADMR: A Deep Learning based Anomaly Detection for MANET Routing

We developed DeepADMR, a novel neural anomaly detector for the deep reinforcement learning (DRL)-based DeepCQ+ MANET routing policy. The performance of DRL-based algorithms such as DeepCQ+ is only verified within the trained and tested environments, hence their deployment in the tactical domain indu...

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Bibliographic Details
Main Authors: Yahja, Alex, Kaviani, Saeed, Ryu, Bo, Kim, Jae, Larson, Kevin
Format: Conference Proceeding
Language:English
Subjects:
Deep learning
Detectors
Radio frequency
Real-time systems
Reinforcement learning
Routing
Training
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Summary:We developed DeepADMR, a novel neural anomaly detector for the deep reinforcement learning (DRL)-based DeepCQ+ MANET routing policy. The performance of DRL-based algorithms such as DeepCQ+ is only verified within the trained and tested environments, hence their deployment in the tactical domain induces high risks. DeepADMR monitors unexpected behavior of the DeepCQ+ policy based on the temporal difference errors (TD-errors) in real-time and detects anomaly scenarios with empirical and non-parametric cumulative-sum statistics. The DeepCQ+ design via multi-agent weight-sharing proximal policy optimization (PPO) is slightly modified to enable the real-time estimation of the TD-errors. We report the DeepADMR performance in the presence of channel disruptions, high mobility levels, and network sizes beyond the training environments, which shows its effectiveness.
ISSN:2155-7586
DOI:10.1109/MILCOM55135.2022.10017842