# Combating Advanced Persistent Threats: Challenges and Solutions

arXiv: https://arxiv.org/abs/2309.09498 · HTML: https://mlsanity.com/paper/2309.09498/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2023.

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

- [From One Attack Domain to Another: Contrastive Transfer Learning with Siamese Networks for APT Detection](https://mlsanity.com/paper/2511.20500/) (2025)
- [Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications](https://mlsanity.com/paper/2505.19837/) (2025)
- [Cyber Deception: State of the art, Trends and Open challenges](https://mlsanity.com/paper/2409.07194/) (2024)
- [Preliminary Investigation into Uncertainty-Aware Attack Stage Classification](https://mlsanity.com/paper/2508.00368/) (2025)

## Abstract (arXiv)

The rise of advanced persistent threats (APTs) has marked a significant cybersecurity challenge, characterized by sophisticated orchestration, stealthy execution, extended persistence, and targeting valuable assets across diverse sectors. Provenance graph-based kernel-level auditing has emerged as a promising approach to enhance visibility and traceability within intricate network environments. However, it still faces challenges including reconstructing complex lateral attack chains, detecting dynamic evasion behaviors, and defending smart adversarial subgraphs. To bridge the research gap, this paper proposes an efficient and robust APT defense scheme leveraging provenance graphs, including a network-level distributed audit model for cost-effective lateral attack reconstruction, a trust-oriented APT evasion behavior detection strategy, and a hidden Markov model based adversarial subgraph defense approach. Through prototype implementation and extensive experiments, we validate the effectiveness of our system. Lastly, crucial open research directions are outlined in this emerging field.