# Attackers Strike Back? Not Anymore -- An Ensemble of RL Defenders Awakens for APT Detection

arXiv: https://arxiv.org/abs/2508.19072 · HTML: https://mlsanity.com/paper/2508.19072/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 26 Aug 2025.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

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

- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [Deep Reinforcement Learning for Cyber Security](https://mlsanity.com/paper/1906.05799/) (2019)
- [A baseline for unsupervised advanced persistent threat detection in system-level provenance](https://mlsanity.com/paper/1906.06940/) (2019)
- [Provenance-based Intrusion Detection: Opportunities and Challenges](https://mlsanity.com/paper/1806.00934/) (2018)
- [A Rule Mining-Based Advanced Persistent Threats Detection System](https://mlsanity.com/paper/2105.10053/) (2021)
- [Hack Me If You Can: Aggregating AutoEncoders for Countering Persistent Access Threats Within Highly Imbalanced Data](https://mlsanity.com/paper/2406.19220/) (2024)
- [Categorical anomaly detection in heterogeneous data using minimum description length clustering](https://mlsanity.com/paper/2006.07916/) (2020)

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

- [From One Attack Domain to Another: Contrastive Transfer Learning with Siamese Networks for APT Detection](https://mlsanity.com/paper/2511.20500/) (2025)

## Abstract (arXiv)

Advanced Persistent Threats (APTs) represent a growing menace to modern digital infrastructure. Unlike traditional cyberattacks, APTs are stealthy, adaptive, and long-lasting, often bypassing signature-based detection systems. This paper introduces a novel framework for APT detection that unites deep learning, reinforcement learning (RL), and active learning into a cohesive, adaptive defense system. Our system combines auto-encoders for latent behavioral encoding with a multi-agent ensemble of RL-based defenders, each trained to distinguish between benign and malicious process behaviors. We identify a critical challenge in existing detection systems: their static nature and inability to adapt to evolving attack strategies. To this end, our architecture includes multiple RL agents (Q-Learning, PPO, DQN, adversarial defenders), each analyzing latent vectors generated by an auto-encoder. When any agent is uncertain about its decision, the system triggers an active learning loop to simulate expert feedback, thus refining decision boundaries. An ensemble voting mechanism, weighted by each agent's performance, ensures robust final predictions.