# DeepADMR: A Deep Learning based Anomaly Detection for MANET Routing

arXiv: https://arxiv.org/abs/2302.13877 · HTML: https://mlsanity.com/paper/2302.13877/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 24 Jan 2023.

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Policy Distillation](https://mlsanity.com/paper/1511.06295/) (2015)
- [True Online Temporal-Difference Learning](https://mlsanity.com/paper/1512.04087/) (2015)
- [DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks](https://mlsanity.com/paper/2111.15013/) (2021)
- [Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for MANETs](https://mlsanity.com/paper/2101.03273/) (2021)
- [Universal Data Anomaly Detection via Inverse Generative Adversary Network](https://mlsanity.com/paper/2001.08809/) (2020)
- [A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing](https://mlsanity.com/paper/2105.06289/) (2021)

## Built on by (most cited first, 1 of 1)

- [Collaborative Information Dissemination with Graph-based Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2308.16198/) (2023)

## Abstract (arXiv)

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.