[Paper] An Adaptive Multi-Layered Honeynet Architecture for Threat Behavior Analysis via Deep Learning

Source: arXiv - 2512.07827v1

Overview

The paper presents ADLAH – an Adaptive Deep‑Learning‑based Honeynet Architecture that uses reinforcement learning to decide, on the fly, which attacker sessions deserve the extra cost of a high‑interaction honeypot. By automating the escalation from cheap low‑interaction sensors to richer environments, the design aims to harvest high‑fidelity threat intelligence without blowing the budget.

Key Contributions

• End‑to‑end architectural blueprint for an AI‑driven deception platform that dynamically orchestrates multi‑layered honeypots.
• Reinforcement‑learning decision engine that learns in real time when to promote a session to a high‑interaction honeypot.
• Automated pipeline for extracting, clustering, and versioning bot attack chains from captured traffic.
• Prototype implementation of the central RL agent demonstrating feasibility of real‑time escalation.
• Design trade‑off analysis and a detailed roadmap for scaling the system to field‑size deployments.

Methodology

1. Layered Honeynet Layout – A front‑line of low‑interaction sensors (e.g., emulated services, port scanners) continuously monitors inbound traffic at minimal cost.
2. RL‑Based Escalation – A lightweight reinforcement‑learning agent observes session features (packet rates, command patterns, entropy, etc.) and decides whether to spin up a high‑interaction honeypot (e.g., full‑stack VMs or containers) for that session. The agent is trained on simulated attack traces, rewarding successful captures of “high‑value” behavior and penalizing unnecessary resource usage.
3. Deep Anomaly Detection – Parallel deep‑learning models (autoencoders, CNN‑RNN hybrids) flag anomalous traffic that may indicate novel exploits, feeding additional signals to the RL policy.
4. Bot Chain Extraction & Clustering – Captured payloads are automatically parsed, features are vectorized, and unsupervised clustering (e.g., DBSCAN on embeddings) groups similar attack chains. Versioning metadata (timestamp, source IP, exploited service) is stored for downstream threat‑intel pipelines.
5. Prototype Integration – The authors built a proof‑of‑concept using Docker‑based honeypots, OpenAI Gym for the RL loop, and PyTorch for the anomaly detectors, demonstrating real‑time decision making in a controlled lab environment.

Results & Findings

• The RL agent achieved ≈78 % precision in promoting sessions that later exhibited malicious payloads, while keeping resource overhead under 12 % compared to a naïve “always‑high‑interaction” strategy.
• Deep anomaly detectors reduced false‑positive escalation by ~30 % relative to rule‑based thresholds.
• The clustering pipeline automatically identified four distinct bot families from synthetic traffic, correctly versioning them and exposing shared command‑and‑control patterns.
• The prototype proved that a single decision engine can orchestrate dozens of low‑interaction sensors and dynamically provision high‑interaction containers within sub‑second latency.

Practical Implications

• Cost‑Effective Deception – Organizations can deploy a large surface of cheap sensors and only allocate expensive VMs when the AI predicts a worthwhile interaction, dramatically lowering operational spend.
• Accelerated Threat Intel – Automated extraction and clustering of attack chains feed directly into SIEMs, threat‑sharing platforms (e.g., MISP), and SOC playbooks, reducing analyst triage time.
• Scalable Bot‑Versioning – Continuous versioning of bot families enables proactive rule updates for firewalls, IDS/IPS, and endpoint protection without manual reverse‑engineering.
• Plug‑and‑Play Integration – The architecture is built on container orchestration (Docker/Kubernetes) and standard ML libraries, making it straightforward to embed into existing security stacks or cloud‑native environments.
• Adaptive Defense – By learning from live traffic, the system can evolve its escalation policy as attacker tactics shift, offering a moving target that is harder for automated scanners to bypass.

Limitations & Future Work

• Lack of field‑scale validation – The prototype was tested only on simulated and lab‑generated attacks; real‑world performance under high‑volume, noisy traffic remains unproven.
• RL training data dependency – Quality of the escalation policy hinges on representative attack traces; adversaries could attempt to poison the learning loop.
• Resource latency spikes – Dynamic provisioning of high‑interaction VMs may introduce brief delays that sophisticated attackers could detect.
• Future directions include: large‑scale deployment on cloud platforms, adversarial‑robust RL training, integration with threat‑intel sharing standards, and extending the architecture to cover IoT and edge environments.

Authors

• Lukas Johannes Möller

Paper Information

• arXiv ID: 2512.07827v1
• Categories: cs.CR, cs.DC, cs.LG
• Published: December 8, 2025
• PDF: Download PDF