# Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy

arXiv: https://arxiv.org/abs/1811.04539 · HTML: https://mlsanity.com/paper/1811.04539/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2018.

## Built on by (most cited first, 4 of 4)

- [DeepGuard: A Framework for Safeguarding Autonomous Driving Systems from Inconsistent Behavior](https://mlsanity.com/paper/2111.09533/) (2021)
- [Differential Analysis of Triggers and Benign Features for Black-Box DNN Backdoor Detection](https://mlsanity.com/paper/2307.05422/) (2023)
- [Decision-making of Emergent Incident based on P-MADDPG](https://mlsanity.com/paper/2203.12673/) (2022)
- [Advancing Human-Machine Teaming: Concepts, Challenges, and Applications](https://mlsanity.com/paper/2503.16518/) (2025)

## Abstract (arXiv)

The paper proposes an on-line monitoring framework for continuous real-time safety/security in learning-based control systems (specifically application to a unmanned ground vehicle). We monitor validity of mappings from sensor inputs to actuator commands, controller-focused anomaly detection (CFAM), and from actuator commands to sensor inputs, system-focused anomaly detection (SFAM). CFAM is an image conditioned energy based generative adversarial network (EBGAN) in which the energy based discriminator distinguishes between proper and anomalous actuator commands. SFAM is based on an action condition video prediction framework to detect anomalies between predicted and observed temporal evolution of sensor data. We demonstrate the effectiveness of the approach on our autonomous ground vehicle for indoor environments and on Udacity dataset for outdoor environments.