[This is a Guest Diary by Austin Bodolay, an ISC intern as part of the SANS.edu BACS program]
Over the past several months, I have gained practical insight into the challenges of deploying and operating a honeypot, even within a relatively simple environment. This work highlighted how varying hardware, software, and network design—can significantly alter outcomes. Through this process, I observed both the value and the limitations of log collection. Comprehensive telemetry proved essential for understanding activity targeting the honeypot, yet it also became clear that improperly scoped or poorly interpreted logs can produce misleading conclusions. Prior to this research, I had almost no interaction with AI tools and struggled to identify practical ways to integrate them into my work. Throughout this experience, however, AI proved most valuable not as an automated solution, but as a collaborative aid—providing quick syntax on the CLI, offering alternative perspectives, and helping maintain analytical focus.
Introduction
The DShield honeypot is a sensor that pretends to be a vulnerable system exposed to the internet. It collects information from scans and attacks that are often automated, providing insight to analyst what threat actors are targeting and how. The honeypot generates a large amount of data, much of it low-value. Deciding what is meaningful, what separate events are related, and what (if any) actions should be taken. Being able to accurately assess the data requires the right information. And in the event a true incident does occur, being able to piece together the breadcrumbs requires the data is actually there. Piecing it all together requires the right methodology. Using an AI, like ChatGPT, is extremely helpful in tying these concepts together.
The Data: What Was Collected and Why
In the few months my SIEM has collected 8 million logs from 14,000 unique IP addresses. There is a lot of noise on the internet from automated scanners and toolkits that frequently repeat the same actions to every device willing to listen. This constant "background noise" on the internet are systems constantly scanning for what is available, what is potentially vulnerable, and what is low hanging fruit that can provide a foothold for something more. Is there an exposed administrative panel? Do these default credentials work anywhere? And if so, what does information does this hold or what does it have access to? Is this a developer? Does the system have private information worth value? The honeypot sensor provides a way to analyze this traffic to better understand what threat actors are after and how they are going after it.
The basic information that is collected on the honeypot includes source IP addresses, port, protocol, URL, and a few other metrics. The logs primarily record the traffic that was sent to the honeypot. If your router dropped the packets or failed to send them to the honeypot, the logs will not be generated to be sent to the SIEM. The NetFlow logs add a little extra information, like the direction of the packets, the byte count, and packets that were dropped before reaching the honeypot. What my current system does not show is the actual payloads in the traffic, the headers of packets, or the exploit details. ChatGPT helped identify what type of data I actually have, what types of conclusions can be drawn from this data, and methods to validate these conclusions. ChatGPT also identified dead ends early on, saving me time from going down rabbit holes by pointing out the current data will never be able to positively affirm any conclusion.
Part One:
I came across a log that raised some concerns. After providing simple details of the devices involved, the type of log generated, clarifying the log is on the gateway and not the SIEM, and the values recorded in the data, ChatGPT provided insights as to what likely generated this traffic and why it likely isn't an alternative event. I performed additional research to confirm this information is true.
Interaction with ChatGPT
Part Two:
Researching a unique User-Agent "libredtail-http", I began checking a high level how frequently this shows up. I noticed that in several months of logs, this User-Agent appeared for the first time on my sensor in December of 2025. There are 34 unique IP addresses that have used it, most of which have less than 100 events. Interestingly, all events occur on the same days, with up to 2 weeks of silence between the next set of events. Additionally, the URL request and payload sizes were identical among all events, regardless of the source IP address. When researching the User-Agent string "libredtail-http", I came across many articles about malware. Sharing some of the information found with ChatGPT, it quickly identified what I was likely seeing, who it is targeting, what makes an event vulnerable to the attacks, and how to protect from them. More likely than malware, what I was seeing is an automated multi-staged toolkit that is scanning the internet for vulnerable Apache servers, Linux web interfaces, and IoT devices. The source of scans is using low-cost methods to rotate through IP addresses, combined with intermittent campaign timing (burst -> idle -> burst) to reduce detection and attribution. This is likely a botnet and the goal is to enroll new systems into the botnet for additional scanners, proxies, and DDoS nodes. I then began researching this information, such as the CVE mentioned by ChatGPT, indicators of compromise (IOCs), and comparing various sources to what I have in my logs to validate the accuracy of the statements. The responses were very accurate. Had I not used ChatGPT, I would have started searching for IOCs in my logs for signs of malware mentioned in the articles and possibly wasted several hours. I likely would have come to a similar conclusion, but I admit it would have used a lot of my time.
Interaction with ChatGPT based on findings above.
I have found the most value comes by clearly stating what your objective is. The more details provided early on reduce vague answers.
Conclusion and Lessons Learned
Having more logs doesn't equal more answers. If a system is comprised and reaches out to a malicious server, having logs of only incoming traffic won't ever catch this malicious activity. And if you have logs showing a connection with a large volume of data outgoing, but the logs don't include the actual content in the packets, it's nearly impossible to know what was actually inside those packets. And if you are tasked with reviewing tens of thousands or millions of logs, it’s nice to have some help. Consider the use of central logging, something like a SIEM, combined with reaching out to a team member for some help if you are part of a team.
[1] https://chatgpt.com/
[2] https://github.com/bruneaug/DShield-Sensor: DShield Sensor Scripts
[3] https://github.com/bruneaug/DShield-SIEM: DShield Sensor Log Collection with ELK
[4] https://blog.cloudflare.com/measuring-network-connections-at-scale/
[5] https://www.cve.org/CVERecord?id=CVE-2021-42013
[6] https://nvd.nist.gov/vuln/detail/CVE-2021-41773
[7] https://blog.qualys.com/vulnerabilities-threat-research/2021/10/27/apache-http-server-path-traversal-remote-code-execution-cve-2021-41773-cve-2021-42013
[8] https://www.cisa.gov/news-events/cybersecurity-advisories/aa24-016a
[9] https://www.sans.edu/cyber-security-programs/bachelors-degree/
Note: ChatGPT was used for assistance.
-----------
Guy Bruneau IPSS Inc.
My GitHub Page
Twitter: GuyBruneau
gbruneau at isc dot sans dot edu
[This is a guest diary contributed by Claire Perry (LinkedIn)]
The structural integrity of modern society is predicated upon a dense and often opaque network of interconnected systems. For decades, the modeling of these systems remained siloed within specific domains: industrial processes were governed by the hierarchical constraints of the Purdue Model, while corporate and data-centric ecosystems were organized using various Enterprise Architecture (EA) frameworks (Fortinet, n.d.; The Open Group, n.d.). However, the accelerating convergence of Information Technology (IT) and Operational Technology (OT) has exposed a critical analytical gap. Disruptions in the external utility grid, once considered an unlikely factor, now propagate through the physical and logical layers of the enterprise with devastating speed, as evidenced by recent power-related disconnections of large-scale data center operations (Mural et al., 2026; Islam et al., 2023).
To bridge this gap, this report introduces the Comprehensive Linkage and Architectural Infrastructure Resiliency (CLAIR) Model. The CLAIR Model is a new conceptual framework that synthesizes the vertical depth of the Purdue Enterprise Reference Architecture (PERA) with the multi-dimensional, interrogative breadth of the Zachman Framework for Enterprise Architecture (Fortinet, n.d.; The Open Group, n.d.). By establishing a unified taxonomy that accounts for everything from the sub-physical utility grid to the hyper-distributed cloud, the CLAIR Model provides a structured scope for identifying and visualizing critical infrastructure interdependencies. This framework prioritizes the identification of these linkages over specific mitigations, offering a diagnostic tool for understanding how failures in one sector, such as the power grid, generate cascading effects across the data center and manufacturing landscapes (Fortinet, n.d.; Islam et al., 2023; Virginia Department of Emergency Management, n.d.).
The conceptual origin of industrial modeling lies in the 1990s at Purdue University, where researchers developed the Purdue Enterprise Reference Architecture (PERA) to standardize computer-integrated manufacturing (Fortinet, n.d.). The Purdue Model established a functional hierarchy ranging from Level 0 (physical processes) to Level 4 (business logistics), effectively creating an "automation pyramid." Isolation of sensitive controllers from internet-facing business networks is typically achieved via a "demilitarized zone" (DMZ) at Level 3.5 (Fortinet, n.d.).
While the Purdue Model excels at describing the internal dependencies of a single plant, it is inherently insular. It treats the external world as a series of inputs (Level 0) or external services (Level 5) without mapping the complex, bidirectional relationships between the plant and the broader infrastructure (Cybersecurity and Infrastructure Security Agency, 2025a; Williams, 1994). In parallel, Enterprise Architecture (EA) frameworks like Zachman were developed to organize the design artifacts of complex organizations from multiple stakeholder perspectives (The Open Group, n.d.). The CLAIR Model recognizes that neither framework, in isolation, can characterize the risks of a "system-of-systems" environment (Department of Defense, 2008). In modern critical infrastructure, a data center is not merely a facility at Level 4 of the Purdue Model; it is a massive electric load at the intersection of global telecommunications, regional power grids, and local water supply systems (UK Parliament, 2025; Chen et al., 2025). Failure to understand these dynamics results in ineffective response and poor coordination between decision-makers (Dudenhoeffer et al., 2006).
The CLAIR Model: Structural Hierarchy and Extended Levels
The CLAIR Model expands the traditional five-level Purdue hierarchy into a ten-level architectural stack. This expansion is designed to capture the "Level -1" dependencies on primary utility infrastructure and the "Level 6" and "Level 7" dependencies on cloud and safety systems (CISA, 2025a; Russo, 2022).
| CLAIR MODEL: 10-Level Architectural Stack | |||
|---|---|---|---|
| Level | Layer | Description | Typical assets |
| >7 | High-Trust / Safety Systems | Ultimate integrity & safe-state maintenance | SIS, DNSSEC, Digital root of trust |
| 6 | The Connected World | External cloud & distributed services | AWS/Azure, IIoT platforms,external VPNs |
| 5 | Corporate Enterprise | Business planning & enterprise services | ERP, HR portals, BI/analytics |
| 4 | Business Operations | Resource Management & Workflow Execution | Workflow tools, Data Repositories, Reporting |
| 3.5 | Operational Boundary / Industrial DMZ | IT-OT convergence, traffic filtration, System Integration &Traffic Management | Firewalls, proxies, IPS/IDS, jump hosts, Security Gateways |
| 3 | Site Operations, Local Management | Facility-wide control, monitoring, Real-time System Oversight | Management Servers, Local Configuration Tools, SCADA servers, |
| 2 | Supervisory Control/Direct Control | Local, Immediate System Monitoring & Adjustment | HMI/SCADA clients, User Interfaces, Supervisory Applications |
| 1 | Core Function | Automated regulation & Execution of Primary Tasks | PLCs, RTUs, IEDs, Embedded Logic, Specialized Processors |
| 0 | Environment Interface | Real-time interaction with the physical world | Input/Output Devices, Sensors, Scanners |
| -1 | Primary Infrastructure | External utility generation &distribution | Power grid, Water, Pipelines, Network Backbones, Core Communication |
The inclusion of Level -1 acknowledges that the "physics" of Level 0 is entirely dependent on a primary technology layer that exists outside the control of the plant operator (Islam et al., 2023). In the CLAIR Model, Level -1 encompasses the electricity generation and transmission systems, which exhibit complex dynamic behaviors such as low inertia and harmonic distortion when interfacing with data center power electronics (Chen et al., 2025). This layer is the source of cascading failure triggers, where a line fault in the high-voltage grid necessitates immediate load redistribution, often leading to voltage fluctuations that destabilize Level 0 sensors and Level 1 controllers (Islam et al., 2023).
Levels 0–5 are generally within the organization’s direct control because the systems, assets, and processes at these layers are typically owned and/or administered by the business, company, or government entity. However, even within this “control zone,” organizations still inherit external dependencies, especially for software, firmware, and operating systems that rely on vendor-provided patches and updates. If an update is delayed, unavailable, or operationally difficult to deploy, the organization may remain exposed to known vulnerabilities or be forced to rely on temporary mitigations until a corrective patch can be implemented (Souppaya & Scarfone, 2022). As a result, these layers may appear internally controlled while quietly depending on upstream providers and external services that introduce risk across otherwise well-managed environments.
As organizations move toward "Smart Factories" and "Hyperscale Data Centers," the reliance on Level 6 (The Connected World) becomes absolute (CISA, 2025a). This level includes the Cloud-Fog-Edge computing model, where instant processing occurs at the edge but long-term analytics and orchestration reside in the cloud (CISA, 2025a). Level 7 represents the "Safety and High-Trust" layer, which is isolated even from the corporate enterprise to ensure that catastrophic failures at lower levels do not prevent a safe system shutdown (Russo, 2022). Level 7 are systems or items that are critical to restoration of levels 0-6 within the organization. The loss of level 7 is a catastrophic issue.
The CLAIR Model maps its ten levels against the six interrogatives of the Zachman Framework to identify dependencies across different dimensions of the infrastructure (The Open Group, n.d.).
The CLAIR Model demonstrates that power grid failures are not merely physical events; they are systemic crises. Data centers are emerging as prominent large electric loads with demand patterns characterized by high power density (Mural et al., 2026; Chen et al., 2025).
A cascading failure is a sequence where one component malfunction triggers successive failures in a "domino mechanism" (Islam et al., 2023). Within the CLAIR framework:
The CLAIR Model categorizes every identified link into a matrix of dependency types. This taxonomy is essential for understanding the nature of the vulnerability.
| Dependency Type | Nature of the Link | Impact Mechanism | Example in CLAIR |
|---|---|---|---|
| Physical | Material transfer | Functional failure due to lack of inputs | Level -1 power supplying Level 0 servers |
| Cyber | Information transfer | Loss of control or visibility | Level 6 cloud service providing ML insights to Level 1 |
| Geographic | Shared location | Common-cause failure (e.g., flood) | Power and fiber sharing a common utility trench |
| Logical | Policy/Regulation | Change in operational state due to external mandate | Utility load-shedding during a heatwave |
To visualize inbound and outbound data dependencies, organizations can use Sankey Flow Maps; flow diagrams that represent transfers or reliance relationships using variable-width links, where wider flows indicate greater magnitude or criticality (Schmidt, 2008). Rather than ranking sensitivities as standalone bars, this method makes dependency direction and coupling immediately visible by placing the system-of-interest at the center and showing weighted flows entering and exiting it.
???????
In practice, each flow can be assigned a dependency “weight” (e.g., criticality, volume, recovery difficulty, or a composite score), enabling teams to quickly identify high-consequence dependencies and prioritize resilience, monitoring, redundancy, and governance controls.
The integration of AI across levels creates new interdependencies. AI models at the operational layers (0-3) introduce risks such as data quality dependency, model drift, and an explainability gap (ASD, 2024). To maintain resilience, the CLAIR Model incorporates operational constraints like the "80% bandwidth rule," ensuring that data aggregation for AI training does not exceed network capacity to protect critical control signals at Level 1 (ASD, 2024).
When AI models are deployed at the operational layers (0-3), they introduce failure mechanisms not present in traditional deterministic system:
The "Why" of the CLAIR Model is increasingly driven by policy, such as the National Security Memorandum on Critical Infrastructure Security and Resilience (NSM-22) (Congressional Research Service, 2024). This framework groups infrastructure functions into four areas: connect, distribute, manage, and supply, which the CLAIR Model maps to specific assets and their dependencies across the stack (CRS, 2024; CISA, 2025b).Maturity and Assessment in the CLAIR Framework
To evaluate the strength of identified dependencies, the CLAIR Model adopts maturity indicator levels (International Atomic Energy Agency [IAEA], 2021).
Impact on Dependency Risk Real-time visualization across the entire stack
| Maturity Level | Characteristic in CLAIR | |
|---|---|---|
| MIL 0 | No implementation | Opaque dependencies; unpredictable failure |
| MIL 1 | Ad hoc / Informal | Some visibility; no standardized monitoring |
| MIL 2 | Consistent / Monitored | Mapped dependencies; defined failure thresholds |
| MIL 3 | Fully Integrated | |
A key insight is that resilience is only as strong as its weakest link. If a data center has MIL 3 resilience at Level 5 but relies on a Level -1 power source with MIL 0 monitoring, the overall system resilience is effectively MIL 0 (IAEA, 2021).
The CLAIR Model synthesis of the Purdue Model and Enterprise Architecture moves beyond a narrow view of internal security toward a holistic understanding of infrastructure interdependencies (CISA, 2025a). It demonstrates that the impact of a power grid failure on data centers is multi-dimensional, involving transients at Level -1, sensor failure at Level 0, and business discontinuity at Level 4 (Mural et al., 2026; Islam et al., 2023). By focusing on these linkages, from the physics of the grid to the logic of the cloud, architects can finally visualize the "walking failures" that define our interconnected world (Islam et al., 2023; CISA, 2025b).
References
Australian Signals Directorate. (2024). Principles for the secure integration of artificial intelligence in operational technology. Cyber.gov.au. Accessed January 26, 2026.
Chen, X., Wang, X., Colacelli, A., Lee, M., & Xie, L. (2025). Electricity demand and grid impacts of AI data centers: Challenges and prospects. Accessed January 22, 2026.
Congressional Research Service. (2024). National security memorandum on critical infrastructure security and resilience (NSM-22). Accessed January 28, 2026.
Cybersecurity and Infrastructure Security Agency. (2025a). Infrastructure resilience planning framework (IRPF) primer. Accessed January 18, 2026.
Cybersecurity and Infrastructure Security Agency. (2025b). Infrastructure resilience planning framework (IRPF) v3.17.2025. Accessed January 30, 2026.
Department of Defense. (2008). Systems engineering guide for systems of systems (Version 1.0). Accessed January 20, 2026. Dudenhoeffer, D. D., Permann, M. R., & Manic, M. (2006).
CIMS: A framework for infrastructure interdependency modeling and analysis. Winter Simulation Conference. Accessed January 23, 2026. Fortinet. (n.d.).
What is the Purdue model for ICS security?. Fortinet.com. Accessed January 13, 2026. International Atomic Energy Agency [IAEA]. (2021).
Maturity-model-paper-ICONS. Accessed January 30, 2026. Islam, M. Z., Lin, Y., Vokkarane, V. M., & Venkataramanan, V. (2023).
Cyber-physical cascading failure and resilience of power grid: A comprehensive review. Frontiers in Energy Research. Accessed January 16, 2026. Macaulay, T. (2025).
The danger of critical infrastructure interdependency. CIGI Online. Accessed January 25, 2026.
Mural, R., Pherwani, D., Gupta, C., Yu, Y., Takahashi, A., Kim, D., Majumder, S., Lee, H., Yu, M., & Xie, L. (2026).
AI, data centers, and the U.S. electric grid: A watershed moment. Belfer Center for Science and International Affairs. Accessed January 15, 2026.
Natural Hazards Review. (2021). Overview of interdependency models of critical infrastructure for resilience assessment (Vol. 23, No. 1). Accessed January 29, 2026.
Russo, S. (2022). Industrial DMZ and zero trust models for ICS. AMS Laurea. Accessed 10 January 24, 2026.
Shuvro, R. A., Das, P., Jyoti, J. S., Abreu, J. M., & Hayat, M. M. (2023). Data-integrity aware stochastic model for cascading failures in power grids. Marquette University. Accessed January 27, 2026.
The Open Group. (n.d.). Mapping the TOGAF ADM to the Zachman framework. Opengroup.org. Accessed January 14, 2026. UK Parliament. (2025).
Data centres: Planning policy, sustainability, and resilience. Accessed January 21, 2026.
Virginia Department of Emergency Management. (n.d.). Understanding critical infrastructure dependencies and interdependencies. Accessed January 17, 2026. Williams, T. J. (1994).
The Purdue enterprise reference architecture (PERA). Industry-Purdue University Consortium. Accessed January 19, 2026. Schmidt, M. (2008).
The Sankey diagram in energy and material flow management, Part I: History. Journal of Industrial Ecology, 12(1), 82–94. https://doi.org/10.1111/j.1530-9290.2008.00004.x Accessed: February 11, 2026
Souppaya, M., & Scarfone, K. (2022). Guide to enterprise patch management planning: Preventive maintenance for technology (NIST Special Publication 800-40 Rev. 4). National Institute of Standards and Technology. Retrieved January 24, 2026, from https://doi.org/10.6028/NIST.SP.800-40r4
" ["live"]=> string(1) "Y" ["serial"]=> int(0) ["id"]=> int(642063) ["storyid"]=> int(32748) ["version"]=> int(1) ["madelive"]=> string(19) "2026-02-26 12:21:26" ["frontpage"]=> string(1) "Y" ["rank"]=> int(5) ["type"]=> string(7) "handler" ["digg"]=> string(0) "" ["ratesum"]=> int(0) ["ratecount"]=> int(0) ["hits"]=> int(0) ["locked"]=> int(0) ["lastreply"]=> string(19) "0000-00-00 00:00:00" ["tweet"]=> string(100) "The CLAIR Model: A Synthesized Conceptual Framework for Mapping Critical Infrastructure Interdepende" ["votes"]=> int(0) ["tweeted"]=> string(1) "Y" ["byline"]=> string(12) "Claire Perry" } array(23) { ["date"]=> string(10) "2021-01-19" ["headline"]=> string(39) "Gordon for fast cyber reputation checks" ["updated"]=> string(19) "2021-01-19 03:15:48" ["text"]=> string(2891) "Gordon is a great website for security analysis and threat intelligence practitioners courtesy of Marc-Henry Geay of France.
It’s a fine offering that quickly provides threat and risk information about observables such as IPv4 addresses, URLs, Domains/FQDNs, MD5, SHA-1, SHA-256 hashes, or email addresses.
All aspirations and architecture for Gordon are available in Marc-Henry’s Medium post, as well as his About content.
You really need only know the following in any detail:
I gave Gordon a quick test using IPv4 IOCs from the Cisco Talos Threat Advisory: SolarWinds supply chain attack. Gordon limits you to 15 observables at most, and note that it favors non-Microsoft browsers, so I experimented via Firefox. Using ten IP IOCs, separated one per line, I received swift results as seen in Figure 1.
.JPG)
Figure 1: Gordon IPv4 SUNBURST results
As noted, Figure 1: shows IPvs SUNBURST IOC results that are precise and color coded by risk.
Using ten SHA-256 hashes from the Talos report for my next query I opted to export the results as an Excel document, then sorted by malicious results only.
.JPG)
Figure 2: Gordon SHA-256 query results
Again, the SUNBURST SHA-256 IOC results are robust and detailed. I’ve certainly added Gordon to my favorites list and suggest you consider doing the same.
Cheers…until next time.
" ["live"]=> string(1) "Y" ["serial"]=> int(0) ["id"]=> int(948565198) ["storyid"]=> int(27004) ["version"]=> int(1) ["madelive"]=> string(19) "2021-01-19 03:15:48" ["frontpage"]=> string(1) "Y" ["rank"]=> int(5) ["type"]=> string(7) "handler" ["digg"]=> string(0) "" ["ratesum"]=> int(0) ["ratecount"]=> int(0) ["hits"]=> int(0) ["locked"]=> int(1) ["lastreply"]=> string(19) "2021-01-22 21:25:27" ["tweet"]=> string(39) "Gordon for fast cyber reputation checks" ["votes"]=> int(6) ["tweeted"]=> string(1) "Y" ["byline"]=> string(10) "Russ McRee" }