Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Lukas Johannes Möller

TL;DR

ADLAH presents an end-to-end, AI-driven adaptive honeynet that escalates suspect sessions from low-interaction MADCAT sensors to high-interaction pods via a reinforcement learning controller. It bridges the gap between static sensors and infrastructure-level deception, offering automated attack-chain extraction, bot versioning, and MITRE ATT&CK mapping for actionable threat intel. The prototype demonstrates real-time deployment decisions, with a design that emphasizes scalability, security, and extensibility, while outlining a roadmap for empirical evaluation and broad deployment. The work highlights practical challenges, including data-path completeness, evaluation in live traffic, and the need for more sophisticated reward mechanisms that balance intelligence quality and quantity.

Abstract

The escalating sophistication and variety of cyber threats have rendered static honeypots inadequate, necessitating adaptive, intelligence-driven deception. In this work, ADLAH is introduced: an Adaptive Deep Learning Anomaly Detection Honeynet designed to maximize high-fidelity threat intelligence while minimizing cost through autonomous orchestration of infrastructure. The principal contribution is offered as an end-to-end architectural blueprint and vision for an AI-driven deception platform. Feasibility is evidenced by a functional prototype of the central decision mechanism, in which a reinforcement learning (RL) agent determines, in real time, when sessions should be escalated from low-interaction sensor nodes to dynamically provisioned, high-interaction honeypots. Because sufficient live data were unavailable, field-scale validation is not claimed; instead, design trade-offs and limitations are detailed, and a rigorous roadmap toward empirical evaluation at scale is provided. Beyond selective escalation and anomaly detection, the architecture pursues automated extraction, clustering, and versioning of bot attack chains, a core capability motivated by the empirical observation that exposed services are dominated by automated traffic. Together, these elements delineate a practical path toward cost-efficient capture of high-value adversary behavior, systematic bot versioning, and the production of actionable threat intelligence.

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

TL;DR

ADLAH presents an end-to-end, AI-driven adaptive honeynet that escalates suspect sessions from low-interaction MADCAT sensors to high-interaction pods via a reinforcement learning controller. It bridges the gap between static sensors and infrastructure-level deception, offering automated attack-chain extraction, bot versioning, and MITRE ATT&CK mapping for actionable threat intel. The prototype demonstrates real-time deployment decisions, with a design that emphasizes scalability, security, and extensibility, while outlining a roadmap for empirical evaluation and broad deployment. The work highlights practical challenges, including data-path completeness, evaluation in live traffic, and the need for more sophisticated reward mechanisms that balance intelligence quality and quantity.

Abstract

The escalating sophistication and variety of cyber threats have rendered static honeypots inadequate, necessitating adaptive, intelligence-driven deception. In this work, ADLAH is introduced: an Adaptive Deep Learning Anomaly Detection Honeynet designed to maximize high-fidelity threat intelligence while minimizing cost through autonomous orchestration of infrastructure. The principal contribution is offered as an end-to-end architectural blueprint and vision for an AI-driven deception platform. Feasibility is evidenced by a functional prototype of the central decision mechanism, in which a reinforcement learning (RL) agent determines, in real time, when sessions should be escalated from low-interaction sensor nodes to dynamically provisioned, high-interaction honeypots. Because sufficient live data were unavailable, field-scale validation is not claimed; instead, design trade-offs and limitations are detailed, and a rigorous roadmap toward empirical evaluation at scale is provided. Beyond selective escalation and anomaly detection, the architecture pursues automated extraction, clustering, and versioning of bot attack chains, a core capability motivated by the empirical observation that exposed services are dominated by automated traffic. Together, these elements delineate a practical path toward cost-efficient capture of high-value adversary behavior, systematic bot versioning, and the production of actionable threat intelligence.

Paper Structure

This paper contains 127 sections, 5 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. The Evolving Cyber Threat Landscape and Strategic Motivation
  3. BSI Collaboration and MADCAT Project Overview
  4. Limitations of Traditional Honeypot Systems
  5. The Promise of Adaptive Honeynets
  6. Research Objectives and Contributions
  7. Scope and Limitations
  8. Document Organization
  9. Terminology and Definitions
  10. Core Concepts
  11. Technical Terms
  12. Network and Security Concepts
  13. Evaluation Metrics
  14. Operational Terms
  15. Related Work
  16. ...and 112 more sections

Figures (4)

  • Figure 1: Ransomware Payment Size Analysis by Chainanalysis. chainanalysis20252025-1cc
  • Figure 2: Visual overview of the adaptive honeynet architecture.
  • Figure 3: Detailed data flow of an adversary session, from initial contact to high-interaction engagement.
  • Figure 4: Visual overview of the prototype's RL-Agent architecture.