In the architectural hierarchy of CyberNEMO, the White Shark probe acts as the primary sensory organ, providing the critical observability required for a secure meta-Operating System. While other components focus on high-level orchestration or AI-driven analysis, White Shark operates at the “ground level,” functioning as a high-precision network probe that monitors the most fundamental unit of connectivity: the network socket.

Technical Architecture and Integration

Technically, White Shark is designed for seamless deployment within Kubernetes-based environments, where it integrates into the data plane to capture real-time telemetry. Its architecture is built around a lightweight footprint to minimize overhead while maintaining the ability to collect granular network metrics across the distributed Computing Continuum. As a key asset within Work Package 2 (WP2), it is specifically engineered to support Zero Trust Network Access (ZTNA) by providing the visibility needed to enforce “explicit verification” for every connection within the cluster.

Point-to-Point Measurement for High Precision

The defining feature of White Shark is its use of point-to-point measurement. Unlike traditional tools that provide broad averages, White Shark retrieves specific metrics—including latency, throughput, and jitter—directly between two communication endpoints. This socket-level approach bypasses the “fog” created by virtual network overlays and high-level abstractions, ensuring that the captured data reflects the actual communication experience of the microservices. This high-fidelity data is essential for differentiating between standard network fluctuations and subtle anomalies.

Driving Intelligence: The Link to NADA

The precision of White Shark is not just for monitoring performance; it is the essential fuel for NADA (Network Anomaly Detection AI). By providing a continuous stream of verifiable point-to-point data, White Shark allows NADA to analyze temporal and contextual patterns with extreme accuracy. Together, they form a proactive security loop: White Shark captures the “ground truth” of the network, and NADA interprets that truth to identify, ensuring the CyberNEMO environment remains resilient and secure.

The transition from the NEMO project to CyberNEMO marks a critical evolution in how we approach network visibility within distributed systems. In the original NEMO project, the primary challenge was establishing reliable performance monitoring across diverse infrastructure. Our response, developed by UPM within the networking work package, was White Shark. White Shark was designed as a network probe, focusing on the fundamental socket layer to measure point-to-point communication metrics like latency, throughput, and jitter. This provided a foundational level of observability, allowing operators to understand how the network was performing at any given moment.

However, as we moved into CyberNEMO, the landscape shifted dramatically. The emergence of a true “computing continuum”—spanning Cloud, Edge, and IoT devices—introduced complexity and a expanded attack surface. Simple performance monitoring was no longer sufficient. We realized that the massive stream of high-fidelity network telemetry generated by White Shark was not just performance data; it was a rich, untapped source of security intelligence. The data that previously told us if the network was fast, could now tell us if the network was being compromised.

This realization led to the development of the NADA (Network Anomaly Detection AI) component in CyberNEMO. NADA represents the intelligent brain that sits atop the White Shark sensing layer. Its purpose is to ingest the granular, socket-level data captured by the probe and use advanced machine learning algorithms to identify temporal and contextual anomalies.

The journey from NEMO to CyberNEMO is therefore characterized by a shift from reactive performance observation to proactive, AI-driven security validation. By enriching the data previously used only for network optimization, we have created a robust mechanism for enforcing Zero Trust principles by design. This evolutionary step ensures that CyberNEMO doesn’t just provide a high-performance network, but a verifiably secure and resilient foundation for the next generation of meta-operating systems.

In a standard Kubernetes (K8s) deployment, the sheer level of abstraction is a double-edged sword. While it simplifies orchestration, it often obscures the granular reality of network traffic. For the CyberNEMO project, specifically within WP2, we move past these high-level views to focus on the network socket. Why? Because sockets represent the “ground truth” of connectivity. In a distributed meta-OS, understanding the real-time state of point-to-point communication is the only way to ensure Cybersecurity and Privacy by Design.

Capturing the “Ground Truth” with White Shark

Traditional Kubernetes monitoring often looks at service-level averages, which can mask micro-bursts of latency or intermittent failures. By monitoring at the socket level, our White Shark probe can collect raw, high-fidelity data—including latency, throughput, and jitter—directly from the source. This allows us to see exactly how data moves between specific pods, bypassing the “fog” of virtualized overlays. This level of precision is essential for building a verifiable data plane, ensuring that every packet follows its intended path without manipulation.

Building a Stronger Zero Trust Foundation

Ultimately, focusing on sockets supports the Zero Trust principle of “explicit verification”. In CyberNEMO, we don’t just trust that a connection is secure because it’s inside the cluster. Instead, we use socket-based telemetry to constantly validate that communication patterns match the intended security policies.

As cyberattacks become more frequent and complex, organizations are turning to Artificial Intelligence (AI) to defend their digital assets. Standard AI is incredibly fast at spotting patterns, but it often works like a “black box”—it might tell a security team, “This file is a virus,” or “there is a cyberattack going on from this IP addresss” without ever explaining why. For a security professional, a simple “Yes” or “No” isn’t enough. If the AI is wrong, it could block an important company document or block services that the company provides; if it’s right, the team still needs to know how the attacker got in to stop it from happening again. This is where Explainable AI (XAI) comes in.

What is Explainable AI (XAI)?

XAI is a set of tools and methods designed to make the “internal thought process” of an AI understandable to humans. In cybersecurity, XAI doesn’t just detect a threat; it provides a rational justification for its decision.

For monitoring assets and detecting attack instead of just monitoring for “bad” things, XAI helps security teams understand what “normal” looks like. If the AI flags a login attempt as suspicious, XAI can point to specific reasons: “The user is logging in from a new country” or “This account is suddenly accessing 2,000 files it never touched before.” XAI can generate maps or charts showing exactly where a network’s behavior deviated from the norm, helping humans spot the “smoking gun” quickly.

For suggesting mitigations XAI doesn’t just sound the alarm; it helps build the shield. By explaining the nature of the attack, it can suggest the best way to stop it.If the AI explains: “This is a Brute Force attack targeting the HR database,” the suggested action is clear: “Temporarily lock the targeted accounts and require a password reset.”

The Importance of the “User-in-the-Loop”

The most critical part of XAI is that it keeps a human—the User-in-the-Loop—at the center of the decision. Cybersecurity is high-stakes; a mistake could shut down a hospital’s network or a city’s power grid. XAI increases trust, facilitates collaboration and provides accountability.

• Trust and Validation: When an AI can explain itself, a human expert can quickly verify if the alert is a real threat or a “false positive” (a mistake).
• Collaboration: Humans bring “common sense” and context that AI lacks. For example, the AI might flag a large data transfer as an attack, but a human knows it’s just the annual company backup. XAI allows the human to see the AI’s logic, agree or disagree, and teach the system to be better next time.
• Accountability: If something goes wrong, XAI provides a clear “paper trail” showing why a certain decision was made, which is essential for legal and safety audits.

The main differences between standard AI and explainable AI (XAI) are the following. In terms of output standard AI could mention that “High Risk is detected” but explainable AI would say “High Risk: Unusual data flow to an unknown IP is detected.” The human role is highly elevated in XAI from blindly trust or ignore the human to review evidence and take informed action. In addition, the learning process becomes stronger because instead of AI algorithms learning alone the human can provide feedback to refine the AI algorithms.

XAI transforms AI from a mysterious oracle into a transparent partner, ensuring that while the computer does the “heavy lifting” of data analysis, the human stays in control of the final defense strategy.

In the complex architecture of the CyberNEMO meta-Operating System, ensuring secure and reliable communication across the computing continuum is paramount. While high-level security frameworks like Zero Trust Network Access (ZTNA) provide the overarching strategy, the actual heavy lifting of data exchange happens at a much more fundamental level: the network socket.

A network socket is essentially an internal endpoint for sending or receiving data at a single node in a computer network. Think of it as a virtual “plug” that allows two different processes—whether they are on the same machine or across the world—to talk to each other. In a Kubernetes (K8s) environment, which serves as the foundation for CyberNEMO’s deployment, sockets are the critical bridges between containerized microservices. They enable the point-to-point communication necessary for workloads to function as a unified system.

Why Sockets Matter for Network Measurement

Within the WP2 (Work Package 2), the focus is on “Cybersecurity and Privacy by Design”. To achieve this, we cannot rely on surface-level metrics. We need to measure real communication at the socket level. This is where components like White Shark come into play.

Originally developed for the NEMO project, White Shark is a specialized network probe designed to collect and retrieve high-fidelity network data. By tapping into socket communication, White Shark can measure point-to-point metrics—such as latency and throughput—directly between two endpoints. This provides a level of precision that traditional network monitors often miss, as it captures the actual data flow as seen by the applications themselves, rather than just the underlying infrastructure.

From Raw Data to Intelligence: The Role of NADA

Capturing socket-level data is only half the battle; the next step is making sense of it. In CyberNEMO, this data is fed into the Network Anomaly Detection AI (NADA). NADA’s purpose is to identify temporal and contextual anomalies—suspicious patterns in the network traffic that could indicate a security breach.