Attention Honeypots: Global Attack Analysis

• Attention Honeypots are specialized decoy systems that simulate diverse network environments to capture and analyze cyber attacks.
• The deployment uses globally distributed low-interaction systems emulating multiple OSes, where statistical models reveal predictive insights from minority attack sources.
• Empirical analysis of propagation and temporal dynamics informs proactive risk mitigation and adaptive defense strategies.

A honeypot is a purposely instrumented computer system or network resource designed to emulate genuine targets and to attract cyber attackers, capturing their activities for empirical paper, profiling, and the refinement of defensive mechanisms. Honeypots leverage deception to solicit, observe, and analyze hostile behavior. By deploying honeypots globally and collecting rich traffic—often via centrally coordinated platforms—they directly support rigorous statistical modeling and characterization of Internet-scale attack processes. This approach yields actionable insights into the temporal dynamics, geographic distribution, and propagation mechanisms of attacks, and offers data-driven strategies for risk mitigation and adaptive defense.

1. Distributed Honeypot Deployment and Data Collection

The empirical basis of this work is the Leurré.com honeypot platform, which comprises 35 globally distributed low-interaction honeypot systems. Each honeypot emulates three distinct operating systems and exposes a diversity of network services, collectively simulating a heterogeneous attack surface. All network packets—comprising both payload and meta-information (source IP, geo-location, operating system, and timestamp)—are streamed to a central repository for detailed statistical analysis.

This distributed deployment enables the aggregation of diverse attack attempts, supporting granular temporal and geographic analyses. The centralized data acquisition architecture increases the fidelity and statistical power of subsequent modeling work, providing a foundation for exploring both local and global properties of attack processes.

2. Statistical Modeling of Attack Processes

Global and Geographic Attack Modeling

The dynamics of observed attacks are formalized through the definition of $Y(t)$, the aggregate rate of attacks per time unit across the platform, and $X_j(t)$, the per-country attack rate for country $j$. A linear regression model is used to reconstruct the global attack activity as a linear combination of country-specific streams:

$Y^*(t) = \sum_{j=1}^k \alpha_j X_j(t) + \beta$

where $\alpha_j$ are the regression coefficients and $\beta$ is a constant offset. The quality of fit is measured with the coefficient of determination,

$$R^2 = \frac{\sum_i (\hat{Y}(i) - \bar{Y})^2}{\sum_i (Y(i) - \bar{Y})^2}$$

where $Y(i)$ are observed counts, $\hat{Y}(i)$ are model estimates, and $\bar{Y}$ is the temporal mean.

Crucially, models utilizing streams from countries with a modest share of total attacks (e.g., Russia, 1.9%; UK, 3.7%) can nonetheless produce a high $R^2$ at the global scale (e.g., $R^2 \approx 0.93$–$0.944$), indicating that minority sources sometimes serve as reliable predictors of overall attack rates.

Temporal Attack Pattern Analysis

Inter-arrival times of consecutive attacks, denoted $t_i$, are statistically characterized. Reliability-theoretic distributions—Weibull, Lognormal, Pareto, and Exponential—are all tested. The best fit is obtained with a mixed model combining Pareto (for heavy tails) and Exponential components:

$$\mathrm{pdf}(t) = P_a \cdot \frac{k}{(t+1)^{k+1}} + (1 - P_a) \cdot \lambda e^{-\lambda t}$$

where $P_a$ is the mixture probability, $k$ the Pareto index, and $\lambda$ the exponential rate constant. Parameters are estimated in R, and Kolmogorov-Smirnov tests confirm the superior fit of this mixture model over a pure exponential (i.e., Poisson process), capturing the "bursty" and heavy-tailed temporal structure of real-world attacks.

3. Propagation Analysis Across Distributed Platforms

Propagation analysis formalizes how attack sources appear sequentially on different honeypot platforms: a propagation event is logged when the same IP is observed attacking multiple platforms in succession. These events construct a directed propagation graph, where nodes are platforms and directed edges represent the empirical probability of attack propagation from one platform to another.

This network-centric perspective uncovers correlations between propagation patterns and topological proximity—for example, platforms on the same /8 network exhibit high propagation likelihood, consistent with the operation of coordinated scanners or self-propagating worms. Such models provide evidence for both opportunistic and systematic spreading strategies among attackers, informing adaptive defense.

4. Attacker Strategy Characterization

Honeypot data facilitates several key findings on attacker behavior:

• Geographic Correlations: Attack rates from certain minor-volume countries align strongly with overall global dynamics, suggesting that their activity is disproportionately informative for modeling purposes.
• Heavy-Tailed Arrivals: Inter-attack times are bursty and heavy-tailed, invalidating the standard Poissonian modeling assumption for attack processes. The mixture distribution reflects alternating periods of inactivity and sudden surges—likely corresponding to coordinated attack campaigns or worms.
• Propagation Patterns: Directed propagation graphs highlight that attacks "travel" between platforms in non-uniform ways. Highly interconnected subgroups of platforms indicate zones of increased coordinated attack risk.

These empirical findings enable both the prediction of attack surges and the dynamic adaptation of mitigation strategies.

5. Statistical Model Application and Security Implications

The derived models and their parameters inform practical cybersecurity enhancements:

• Predictive Early Warning: The mixture model for inter-attack times supports the development of surge predictors, enabling proactive intrusion detection.
• Zone-Specific Defense: Geographic and propagation analysis offers a basis for segmenting defense responses, adjusting security postures dynamically according to empirical risk exposure.
• Threat Model Realism: Modeling based on empirical, globally sourced attack data grounds the design of resilient systems in actual adversarial behavior rather than idealized or outdated attacker models.

The novel application of mixture distributions and the propagation graph architecture in this context establishes new standards for the empirical validation of threat models.

6. Methodological Tools and Limitations

Key analytical workflows involve:

• Centralized data collection from globally distributed, low-interaction honeypots.
• Linear regression and $R^2$ analysis for correlational structure.
• Maximum-likelihood parameter estimation in mixture model fitting, verified via the Kolmogorov-Smirnov goodness-of-fit test.
• Construction of empirical propagation graphs for visual and quantitative propagation analysis.

A plausible implication is that further methodological advances—particularly involving high-interaction platforms—could yield more granular data on post-compromise adversarial behavior, enhancing the behavioral models derived from honeypot deployments.

7. Broader Context and Future Directions

The combination of scalable data collection, advanced statistical modeling, and propagation analysis as demonstrated suggests several paths forward:

• Extending analysis workflows to high-interaction honeypots for in-depth behavioral modeling of attacker post-breach activity.
• Incorporating real-time analytics to inform on-the-fly reconfiguration of platform defenses in response to evolving attack dynamics.
• Integrating empirical findings into zone- or segment-specific network hardening protocols for optimal resource allocation.

The approaches and empirical results described serve as a foundational reference for future honeypot-based security analytics, promoting a migration from theoretical or anecdotal models of Internet attack processes toward those grounded in rigorously validated, high-dimensional observational data.