Honey-Chatbot: Adaptive Cyber Honeypot

• Honey-Chatbot is a cybersecurity honeypot that leverages large language models to emulate operating systems and network utilities in realistic, interactive sessions.
• It integrates a modular architecture—including an LLM interface, command-emulation engine, and adaptive risk-scoring module—for dynamic threat analysis.
• Empirical results indicate prolonged attacker engagement and improved forensic data collection, validating its scalability for organizational security.

A Honey-Chatbot is a cybersecurity honeypot system that leverages LLMs as interactive front-ends to emulate common operating systems, network utilities, and applications. Its primary objective is to engage and monitor adversarial interactions, adapting to attacker tactics while collecting detailed forensic data for defensive analysis. The system architecture and methodologies described by McKee & Noever in "Chatbots in a Honeypot World" (McKee et al., 2023) provide a comprehensive blueprint for implementation, evaluation, and practical deployment across diverse organizational environments.

1. System Design and Architecture

The Honey-Chatbot architecture comprises several modular components integrated to simulate interactive command-line environments:

• LLM Interface: This core component maintains prompt context (up to approximately 8,000 tokens) and sequences user interactions. It interfaces directly with attackers via web or API gateways, parsing commands and system instructions.
• Command-Emulation Engine: Responsible for interpreting commands spanning Linux, MacOS, and Windows shells, as well as application commands (Jupyter, TeamViewer installation) and network tools (nmap, ping, arp).
• Adaptation & Risk Scoring Module: Dynamically evaluates each session with a risk score ($R$) by extracting behavioral features and adjusting the simulation's response realism, verbosity, and deflection strategies based on preset thresholds.
• Logging & Monitoring Subsystem: Captures all command-response pairs with metadata and forwards sanitized records to SIEM or forensic data stores for later analysis.

The interaction flow follows: attacker input $\to$ LLM augmentation (OS context, historical prompts) $\to$ simulated output $\to$ risk scoring and adaptation $\to$ logging. All responses are code-block formatted for fidelity.

2. Task Suite and Interaction Modalities

The system defines ten foundational honeypot tasks, each designed via specific system prompts to elicit realistic tool behaviors:

Task	Layer/Emulated Tool	Example Interaction
A	Linux Shell	pwd, ls, python test.py
B	Jupyter Notebook	notebook cell commands
C	Windows DOS (Admin)	registry operations
D	Windows DOS (User)	standard user commands
E	macOS Terminal	OS X command set
F	TeamViewer on Linux	installation flow
G	Windows CMD DDoS	ping flood invocation
H	PowerShell	timestamp modifications
I	Windows ARP Table Poisoning	arp manipulations
J	Linux nmap Lateral Movement	port scanning, nmap

Each task employs tailored prompt engineering; for example, Task A uses “I want you to act as a Linux terminal…” to ensure appropriate Bash-like output. Representative transcript samples evidence system capability to mimic deep directory listings and legitimate tool behaviors (e.g., nmap scans displaying open ports and service banners).

3. Adaptive Detection and Risk Modeling

A critical Honey-Chatbot feature is adaptive risk scoring based on attacker activity metrics. Feature functions $f_i(A_i)$ quantify aspects such as:

• $f_1$: command diversity per minute
• $f_2$: ratio of destructive commands (e.g., rm, del)
• $f_3$: scanning intensity via network tool invocations
• $f_4$: registry modification occurrences

The cumulative risk score is calculated as:

$R = \sum_{i=1}^n w_i \cdot f_i(A_i)$

Weights ($w_i$) are derived from offline calibration with historical honeypot logs. Thresholds delineate operational modes:

• Normal: $R < T_{innocent}$, full simulation fidelity.
• Forensic: $T_{innocent} \leq R < T_{forensic}$, enhanced logging, metadata and geolocation capture.
• Deflect: $R \geq T_{deflect}$, misleading outputs, response delays, and fictitious file contents.

The adaptation algorithm, as implemented, logs all details per command for post-facto analysis.

4. Evaluation Metrics and Empirical Results

Key performance indicators measure honeypot efficacy:

• Time-to-Conquer Extension ($T_{tc}$): $T_{tc} = T_{honeypot} – T_{baseline}$. A feasibility paper reports $T_{honeypot}$ of 5.8 hours versus $T_{baseline}$ of 2.1 hours, yielding +176% dwell time extension.
• False Positive Rate (FPR): FPR = $FP / (FP + TN)$; observed at 1.2%—within acceptable operational tolerance.
• Attacker Engagement Score ($E$): $$E = \alpha \cdot \#:commands + \beta \cdot max\_directory\_depth + \gamma \cdot session\_time$$. Feasibility data indicate a +152% increase in engagement.

Metric	Baseline Real VM	Honey-Chatbot	Improvement
Time to Conquer (h)	2.1	5.8	+3.7 (+176%)
FPR (%)	–	1.2	acceptable
Attacker Eng. Score	15.4	38.9	+152%

This suggests that dynamic adaptation not only prolongs attacker involvement but amplifies observable behavioral data, increasing the utility for defensive teams.

5. Data Logging and Forensic Analytics

Interaction records are stored in a structured JSON schema, supporting forensic review:

{ "session_id": "uuid-1234", "timestamp": "2023-01-01T12:34:56Z", "attacker_ip": "198.51.100.23", "command": "nmap -sV 10.0.0.5", "os_context": "linux", "mode": "normal", "risk_score": 12.7, "response": "Starting Nmap 7.70...", "features": {"f1":0.2,"f2":0.0,"f3":1.0,"f4":0.0} }

Forensic pipelines ingest logs to map adversarial tactics, techniques, and procedures (TTP) using frameworks such as MITRE ATT&CK. Session clustering and statistical pattern detection flag zero-day TTPs for incident response teams. A plausible implication is that structured output facilitates automated triage and threat intelligence enrichment.

6. Deployment, Integration, and Maintenance

Comprehensive integration across perimeter, host, and data-security layers is recommended:

• Perimeter exposure: Deploy honeypot IPs on unused subnets; create intentional “open port” signals in firewall logs.
• Host-based controls: Employ EDR agents for lateral movement detection on honeypot VM hosts.
• Data hygiene: Ensure dummy data (e.g., fake Jupyter content) contains no real credentials.

Maintaining system effectiveness requires periodic prompt tuning to resist fingerprinting, regular LLM/model upgrades, and ongoing audit of high-risk sessions with weight updates informed by threat intelligence.

7. Limitations, Evasion Techniques, and Research Directions

Operational constraints include LLM hallucinations (implausible directory or version output), susceptibility to timing-based or side-channel evasion, and non-negligible API computational costs. Sophisticated adversaries may use metadata probes, high-volume port scans, or latency comparison to reveal honeypot status.

Future directions identified by McKee & Noever include:

• Extension to emulate perimeter devices (virtual routers, firewalls) through the LLM interface.
• Integration of host-based virus/emulation detectors, with simulated antivirus output.
• Expansion to data security layers employing honeytokens and simulated mission-critical asset environments.

In summary, the Honey-Chatbot framework establishes a scalable, dynamic platform for adversarial engagement, risk-adaptive simulation, and actionable threat intelligence collection. Its design leverages LLMs to produce high-fidelity emulations across operating system, network, and application surfaces, validated by empirical studies and supported by structured logging for defensive operations (McKee et al., 2023).