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.
@article{arxiv.2302.13877,
title = {DeepADMR: A Deep Learning based Anomaly Detection for MANET Routing},
author = {Alex Yahja and Saeed Kaviani and Bo Ryu and Jae H. Kim and Kevin A. Larson},
journal= {arXiv preprint arXiv:2302.13877},
year = {2023}
}