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

TL;DR

This paper presents ADLAH, an AI-driven adaptive honeynet architecture utilizing deep learning and reinforcement learning to dynamically detect, escalate, and analyze cyber threats, aiming for cost-effective, high-fidelity threat intelligence.

Contribution

The paper introduces a novel adaptive honeynet architecture with a reinforcement learning decision mechanism and a comprehensive blueprint for AI-driven deception in cybersecurity.

Findings

Prototype demonstrates real-time session escalation decisions.

Design trade-offs and limitations are thoroughly analyzed.

Provides a roadmap for empirical evaluation at scale.

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.