Nebraska Patent of the Month – January 2026

What is the Nebraska Patent of the Month for January 2026?The Nebraska Patent of the Month is U.S. Patent No. 12,530,505, titled “Dynamic reconfiguration of embedded networks (DREN).” Developed by Fleet Defender, Inc. in Omaha, Nebraska, this technology introduces a “Moving Target Defense” for industrial and military vehicle networks. Unlike traditional static firewalls, DREN actively rotates network protocols in real-time, preventing cyberattackers from mapping or reverse-engineering critical systems.Key Takeaways:

• Innovation: Active obfuscation of CAN bus communication to thwart Zero-Day attacks.
• Impact: Adopted by major carriers like Werner Enterprises and applicable to military JADC2 logistics.
• Tax Eligibility: The development of DREN qualifies for the R&D Tax Credit by meeting the IRS Four-Part Test, as substantiated by Swanson Reed’s compliance methodology.

Introduction: Defining the New Standard in Industrial Cybersecurity

The Nebraska Patent of the Month: February 2026

In the contemporary landscape of industrial innovation, the intersection of cybersecurity and heavy machinery represents one of the most critical frontiers for technological advancement. Within this domain, U.S. Patent No. 12,530,505, titled “Dynamic reconfiguration of embedded networks (DREN),” has emerged as a seminal document. Officially granted on January 20, 2026, following an application filed on July 17, 2023, this patent is assigned to Fleet Defender, Inc., a company headquartered in the burgeoning tech hub of Omaha, Nebraska. The primary inventor, Terry Reinert, has codified a methodology that fundamentally alters the defensive posture of embedded systems.

Recognizing the disruptive potential of this intellectual property, Swanson Reed’s proprietary AI-driven analysis platform has distinguished Patent 12,530,505 as the Nebraska Patent of the Month for February 2026. This accolade is not merely a ceremonial title; it is the result of a rigorous, algorithmic selection process. The AI system screened over 1,000 potential candidates—comprising every patent filed or granted within the jurisdiction during the evaluation period—filtering for specific markers of innovation. Unlike traditional awards based on subjective peer review or marketing visibility, this selection utilized the inventionINDEX metric, which weighs factors such as technical novelty, the breadth of claims, and, crucially, “real-world impact.” The system isolated Patent 12,530,505 because it addresses a systemic vulnerability in the global supply chain with a solution that is both technically sophisticated and immediately applicable to industrial operations.

The Imperative of Real-World Impact and Market Superiority

The selection of the DREN patent underscores a pivotal shift in how intellectual property is valued in the mid-2020s: a move away from theoretical abstraction toward tangible industrial utility. The modern supply chain is increasingly reliant on “connected fleets”—heavy-duty trucks, autonomous logistics vehicles, and military transports that operate as data centers on wheels. However, these assets have historically relied on the Controller Area Network (CAN) bus, a communication standard designed in the 1980s without modern security constraints.

Patent 12,530,505 was selected because it introduces a Moving Target Defense (MTD) mechanism that renders these legacy networks resilient to sophisticated cyber-attacks. While competitors in the automotive cybersecurity space typically rely on static defense mechanisms—such as firewalls that block known bad traffic or Intrusion Detection Systems (IDS) that passively monitor for anomalies—DREN actively reconfigures the network topology in real-time. This dynamic approach creates an asymmetrical advantage for the defender: whereas an attacker typically needs to find only one static vulnerability to succeed, DREN forces the attacker to hit a constantly moving target.

This report provides an exhaustive analysis of the DREN technology, benchmarking its capabilities against prevailing market competitors to demonstrate its superiority. It further explores the patent’s immediate commercial impact through strategic partnerships, such as those with Werner Enterprises, and its implications for national defense. Finally, the report details the financial mechanisms available to support such innovation, specifically analyzing how the development of DREN technology aligns with the Four-Part Test for the Research and Development (R&D) Tax Credit and how Swanson Reed’s methodology facilitates the substantiation of such complex claims.

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The Context of Vulnerability: Anatomy of Embedded Insecurity

To fully appreciate the magnitude of the innovation presented in Patent 12,530,505, one must first understand the inherent fragility of the systems it is designed to protect. The operational backbone of virtually all land-based vehicles, from commercial sedans to main battle tanks, is the Controller Area Network (CAN) bus. Understanding the limitations of this architecture is essential to grasping why the DREN patent is a necessary evolution.

The Legacy of Trust: CAN Bus Architecture

The CAN bus protocol was developed by Bosch in the mid-1980s to solve a wiring problem. As vehicles became more complex, the amount of copper wire needed to connect every switch to every motor became prohibitive. The CAN bus allowed all Electronic Control Units (ECUs) to communicate over a single twisted pair of wires.

This architecture was built on a foundation of absolute trust. In 1986, vehicles were closed systems; there was no wireless connectivity, no Bluetooth, and no cellular uplink. Consequently, the protocol designers prioritized reliability and speed over security.

• Broadcast Messaging: Every message sent on the CAN bus is broadcast to every other node. There is no addressing in the traditional sense; messages have IDs that indicate content (e.g., “Engine RPM”), but not the sender or receiver.
• Lack of Authentication: There is no mechanism within standard CAN to verify the origin of a message. If a compromised headlight controller sends a message claiming to be the engine controller shouting “Stop!”, the brake system will obey without question.
• Plaintext Communication: Data is transmitted in unencrypted frames. Anyone with physical or remote access to the bus can read every signal, reverse-engineer the communication matrix, and inject malicious commands.

The Rise of the Connected Threat Surface

In the decades since CAN was standardized, the operational environment of the vehicle has changed dramatically, while the internal nervous system has remained largely static. Modern “Smart Trucks” are hyper-connected nodes in the Industrial Internet of Things (IIoT). They feature:

• Telematics Units: Sending real-time location and engine health data to fleet managers via 4G/5G.
• Infotainment Systems: Connected to consumer smartphones and cloud services.
• Over-the-Air (OTA) Updates: Interfaces that allow manufacturers to rewrite firmware remotely.

Each of these external connections represents a potential entry point for an attacker. Once an adversary breaches the perimeter—perhaps through a vulnerability in the telematics unit or a compromised Wi-Fi connection—they gain access to the internal CAN bus. Because the internal bus lacks defense-in-depth, the “hard shell, soft underbelly” paradigm applies. An attacker inside the network has free rein to impersonate critical ECUs, suppress safety warnings, or disable brakes.

The Failure of Static Defenses

The industry’s initial response to this growing threat landscape was to apply IT-style security controls to OT (Operational Technology) environments.

• Gateway Firewalls: Manufacturers installed gateways between the “noisy” infotainment bus and the “critical” powertrain bus. While this stops casual interference, sophisticated attackers can tunnel through gateways or exploit the gateway itself. Furthermore, once a critical bus is reached, the firewall offers no further protection.
• Signature-Based Detection: Early automotive security tools relied on databases of “known bad” messages. This is the “antivirus” model. It fails against Zero-Day attacks—novel exploits that have never been seen before and thus have no signature.
• Heuristic Anomaly Detection: More advanced systems use Machine Learning (ML) to learn “normal” driving patterns and flag deviations. While powerful, these systems suffer from false positives. In the trucking industry, a false positive that engages the brakes or shuts down the engine on a highway is a safety hazard, not just an inconvenience.

It is against this backdrop of systemic vulnerability and inadequate static defenses that Fleet Defender filed for Patent 12,530,505. The industry required a solution that did not merely detect intrusions but actively neutralized them without relying on perfect knowledge of the threat.

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Deep Dive Analysis: U.S. Patent 12,530,505 (DREN)

The Dynamic Reconfiguration of Embedded Networks (DREN) patent represents a paradigm shift from passive observation to active obfuscation. It introduces the concept of Cyber Resilience directly into the communication protocol itself.

Core Technical Mechanism: The Adapter

The patent describes an adapter that sits as an intermediary between the embedded systems (ECUs) and the network. This adapter is not a passive filter; it is an active participant in the network arbitration process.

• Protocol Mutation: The defining feature of DREN is its ability to dynamically alter the communication parameters. In a standard CAN network, the “Brake Command” might always be Message ID 0x100. An attacker who spends time reverse-engineering the truck knows this. DREN changes this mapping dynamically. At Time T1, the brake command might be 0x100. At Time T2, it might be 0x4A2.
• Synchronization: Crucially, the patent details methods for synchronizing these changes across authorized nodes without requiring a central server or high-latency handshake. This ensures that legitimate ECUs (Engine, Transmission, Brakes) always know the “language of the moment,” while an unauthorized intruder is left speaking an obsolete dialect.

Active Defense Against Reverse Engineering

One of the most significant claims in the patent is the adapter’s ability to “actively protect against reverse engineering.”

• Reconnaissance Denial: Cyber-attacks follow a “Kill Chain.” The first step is typically Reconnaissance—mapping the network to understand which nodes control which functions. DREN disrupts this phase. By constantly shifting traffic patterns and IDs, DREN prevents the attacker from building a stable map of the network. If the attacker cannot identify the target, they cannot craft a precise exploit.
• Real-Time Thwarting: The abstract explicitly states the system allows “embedded systems to actively thwart cyber-attacks in real-time.” This implies that the system does not wait for a human analyst to review logs. The rejection of unauthorized packets happens at the protocol level. If a packet does not match the current dynamic configuration, it is discarded or invalidated instantly.

Maintaining Mission-Ready Status

For industrial and military users, security cannot come at the expense of availability. A security system that “fails closed” (shuts down the vehicle) is often as dangerous as the cyber-attack itself.

• Operational Continuity: The patent emphasizes allowing the embedded system to “continue operating in mission ready status.” This suggests that DREN includes fail-safe mechanisms or parallel processing capabilities that ensure safety-critical messages are delivered even while the network is under active attack or reconfiguration. This capability is paramount for the target demographic of Fleet Defender: heavy transport and defense logistics.

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Comparative Benchmarking: DREN vs. Competitors

To substantiate the claim that Patent 12,530,505 represents a superior technology, we must benchmark it against the prevailing solutions in the market. The automotive cybersecurity sector includes major players like Upstream Security, Argus Cyber Security, and Karamba Security. While these companies offer robust products, their underlying philosophies differ fundamentally from the Moving Target Defense (MTD) approach of DREN.

The Competitor Landscape

• Competitor Type A: Cloud-Based Analytics (e.g., Upstream Security)

• Mechanism: These solutions collect vast amounts of data from the fleet’s telematics units and analyze it in the cloud. They look for fleet-wide anomalies (e.g., 50 trucks suddenly reporting the same engine error in the same geographic area).
• Strength: Excellent for post-incident forensics and identifying fleet-wide trends.
• Weakness: Latency. By the time the data reaches the cloud, is analyzed, and a mitigation command is sent back to the truck, the damage (e.g., a crash) may already be done. It is reactive.

• Competitor Type B: Embedded IDPS (e.g., Argus Cyber Security)

• Mechanism: Software installed directly on the vehicle’s gateway or Ethernet switch that inspects packets against a set of rules (Deep Packet Inspection).
• Strength: Low latency detection of known threats.
• Weakness: Static Rules. If an attacker finds a way to craft a malicious packet that looks “legal” (e.g., adhering to the protocol structure but containing a malicious payload), the IDPS may let it through. It relies on the defender “guessing” how the attacker will strike.

The DREN Superiority: Asymmetrical Warfare

Patent 12,530,505 flips the script on the attacker through Asymmetry.

• The Static Defense Dilemma: In traditional security, the defender must be successful 100% of the time. The attacker only needs to be successful once.
• The DREN Advantage: By constantly changing the network parameters, DREN forces the attacker to be successful 100% of the time—they must predict the new configuration for every single packet they inject. The defender only needs the attacker to fail once to identify the intrusion.

Feature Comparison Matrix

The following table benchmarks the DREN technology against these competitor archetypes across critical performance metrics relevant to heavy industry.

Metric	Fleet Defender (DREN Patent 12,530,505)	Cloud-Based Analytics (e.g., Upstream)	Embedded Static IDPS (e.g., Argus)
Defense Philosophy	Active / Moving Target	Passive / Analytical	Passive / Rule-Based
Response Latency	Zero-Latency (Pre-emptive): Invalidates attack capability before execution.	High: Dependent on cellular uplink and cloud processing.	Low: But limited to “detection” speed.
Zero-Day Resilience	High: Effective against unknown attacks because the network topology itself is the defense.	Low: Relies on heuristics or signatures of known attack patterns.	Moderate: Can catch protocol violations but misses logic bombs.
Adversarial Cost	Exponential: Attacker must constantly re-learn the changing network map.	Linear: Attacker must obfuscate payload to bypass analysis.	Linear: Attacker needs to find one bypass technique.
Connectivity Dependence	None: Fully autonomous protection at the edge.	Critical: Fails if the vehicle is in a dead zone or jamming environment.	None: Functions locally.
False Positive Impact	Low: Deterministic algorithm (authorized nodes always know the key).	Moderate: Heuristics can mistake rough terrain driving for an attack.	Moderate: Strict rules can block legitimate diagnostic tools.

Why DREN Wins the “Real-World Impact” Criterion

The AI selection for the Nebraska Patent of the Month weighed “real-world impact” heavily. DREN is superior because it addresses the operational reality of trucking and defense.

• Trucks drive through rural Nebraska where cellular signal is non-existent. Cloud-based solutions fail here. DREN works.
• Military vehicles operate in Electronic Warfare (EW) environments where signals are jammed. DREN works.
• Logistics companies operate on razor-thin margins. They cannot afford a false-positive “engine shutdown” caused by an over-aggressive heuristic. DREN’s deterministic reconfiguration avoids this trap.

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Real-World Impact and Future Potentials

The “Nebraska Patent of the Month” award is not a theoretical honor. It reflects the fact that Fleet Defender has successfully translated this intellectual property into commercial and strategic traction.

Strategic Partnership with Werner Enterprises

A definitive validator of the DREN technology’s impact is its adoption by Werner Enterprises, one of the five largest truckload carriers in the United States.

• Investment & Validation: Public financial records indicate that Werner Enterprises has made strategic equity investments in Fleet Defender, Inc. As of December 31, 2022, Werner held an initial investment valued at $250,000. This early-stage capital injection by a primary customer is a strong signal of market validation. It suggests that Werner identified a critical gap in their fleet security that existing commercial solutions could not fill.
• Operational Deployment: Beyond investment, the collaboration involves the deployment of “Neural Sentinel,” the commercial realization of the DREN patent. Press releases highlight the collaboration between Fleet Defender, Werner, and Platform Science (a leading fleet telematics platform). This integration allows Fleet Defender to deploy its “active defense” software across Werner’s massive fleet of over 8,000 trucks via the Virtual Vehicle™ platform.
• Economic Implications: For a carrier like Werner, the “real-world impact” is measured in risk mitigation. A ransomware attack that locks even 10% of their fleet for 24 hours could cost millions in lost revenue and reputational damage. DREN provides an insurance policy against such catastrophic “Black Swan” events.

Military and Defense Applications

The genesis of Fleet Defender and the terminology used in the patent (“mission ready status”) strongly align with military requirements.

• The “Fleet Defender” Legacy: The name itself evokes the role of the F-14 Tomcat, the “Fleet Defender” of the US Navy, designed to protect the carrier group from long-range threats. Similarly, DREN is designed to protect the logistic fleet—the supply trucks and tankers that are the lifeblood of any military operation.
• Tactical Edge Security: In modern warfare, the “rear echelon” is no longer safe. Cyber-attacks can target logistics convoys to halt the supply of ammunition and fuel. The DREN technology allows military land vehicles to operate in contested cyber environments. Its ability to thwart attacks without “phoning home” to a central server is a critical requirement for JADC2 (Joint All-Domain Command and Control) initiatives, where bandwidth is scarce and silence is survival.

Future Potentials: The Autonomous Horizon

The most profound impact of Patent 12,530,505 lies in the future of Autonomous Trucking.

• Liability Transfer: As the industry moves from Level 2 (Driver Assist) to Level 4 (High Automation), the liability for accidents shifts from the human driver to the software/hardware provider. If a Level 4 truck crashes because its steering bus was hacked, the manufacturer is liable. This existential risk makes “probabilistic” security (like IDS) unacceptable. Manufacturers will require “deterministic” security that can prove the system was resilient. DREN offers this.
• Regulatory Compliance (UN R155): The United Nations Regulation No. 155 (UN R155) now requires vehicle manufacturers to have a certified Cyber Security Management System (CSMS). This regulation effectively mandates “security by design.” DREN fits this requirement perfectly, offering a verifiable, architectural defense mechanism rather than a “patch-later” software fix.
• Critical Infrastructure Expansion: The principles of Dynamic Reconfiguration are not limited to trucks. They are applicable to any embedded network. Future potentials include securing power grid substations, municipal water control systems, and medical devices—any environment where “patching” is difficult and reliability is paramount.

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Strategic R&D Tax Credit Analysis

Innovation of the caliber described in Patent 12,530,505 is capital-intensive. The rigorous testing, the development of custom FPGA or software logic, and the integration with vehicular systems require significant investment. The Research and Experimentation (R&D) Tax Credit (IRC Section 41) serves as a vital mechanism for companies like Fleet Defender to recoup these costs and reinvest in further innovation.

However, claiming the R&D credit for software and embedded hardware is complex. It requires strict adherence to the IRS Four-Part Test. Below, we detail how a project developing DREN technology meets these criteria and how specialized advisory methodologies ensure the claim is defensible.

The Four-Part Test Applied to DREN Development

For a project to qualify as “Qualified Research,” it must satisfy all four of the following tests. We will analyze the DREN development through this statutory lens.

Test 1: Permitted Purpose

• Statutory Requirement: The activity must relate to a new or improved business component (product, process, computer software, technique, formula, or invention) held for sale, lease, or license, or used by the taxpayer in its trade or business. The intent must be to improve functionality, performance, reliability, or quality.
• DREN Application: The development of the “Neural Sentinel” adapter or software stack constitutes a new business component. The specific purpose of the research is to improve the reliability and quality of vehicle network security. By creating a system that is resilient to reverse engineering, Fleet Defender is explicitly improving the “performance” of the standard CAN bus architecture.
• Verdict: Satisfied. The intent is clearly functional improvement, not aesthetic or cosmetic.

Test 2: Technological in Nature

• Statutory Requirement: The activity must be performed to discover information that is technological in nature. The process must fundamentally rely on principles of the physical or biological sciences, engineering, or computer science.
• DREN Application: The development of DREN relies heavily on advanced Computer Science (cryptographic synchronization, algorithm complexity theory) and Electrical Engineering (signal timing on the CAN bus, voltage arbitration, microcontroller architecture). The patent itself serves as proof that the work is rooted in “hard science” rather than soft sciences like market research or management studies.
• Verdict: Satisfied. The project relies on engineering and computer science principles.

Test 3: Elimination of Uncertainty

• Statutory Requirement: At the outset of the project, there must be uncertainty concerning the capability to develop the business component, the method of development, or the appropriate design of the business component.
• DREN Application: While the concept of “moving target defense” existed in theory, applying it to a 1980s serial bus protocol (CAN) inside a moving truck introduces significant uncertainty:

• Uncertainty of Capability: Can we reconfigure the network fast enough (e.g., in microseconds) to thwart an attacker without disrupting the safety-critical messages (e.g., brakes) that must arrive every 10 milliseconds?
• Uncertainty of Design: How do we synchronize the “hopping” sequence between the sender and receiver without a central clock that could fail?
• Uncertainty of Method: What is the optimal algorithm to maximize entropy (randomness) while minimizing CPU load on low-power embedded chips?

• Verdict: Satisfied. The team faced significant technical unknowns that could not be resolved by standard practice.

Test 4: Process of Experimentation

• Statutory Requirement: Substantially all (at least 80%) of the activities must constitute a process of experimentation. This involves:

• Identifying the uncertainty.
• Identifying one or more alternatives to eliminate the uncertainty.
• Conducting a process of evaluating the alternatives (modeling, simulation, trial and error).

• DREN Application: The development process for DREN would inherently involve:

• Hypothesis: “Rotating CAN IDs every 50ms will prevent mapping.”
• Experiment: Implementing this on a test bench (Hardware-in-the-Loop).
• Observation/Failure: Observing that 50ms rotation causes packet collisions with the Transmission Control Unit.
• Iteration: Modifying the algorithm to rotate only during “bus idle” time or changing the rotation frequency to 100ms.
• Retesting: Field testing with Werner Enterprises to validate the new hypothesis.
• This systematic cycle of hypothesis, testing, analysis, and refinement is the core of the R&D Tax Credit eligibility.

• Verdict: Satisfied. The patent development required a rigorous experimental process.

The Role of Swanson Reed: Ensuring Claim Integrity

While the DREN project theoretically meets the criteria, the practical reality of claiming the credit involves substantial documentation burdens. The IRS requires “contemporaneous documentation”—records created at the time the work was done, not reconstructed years later. Swanson Reed, as a specialist R&D tax advisory firm, employs specific methodologies to bridge the gap between engineering activity and tax compliance.

A. TaxTrex: The AI Advantage for Documentation

Swanson Reed utilizes TaxTrex, a proprietary AI-driven platform designed to capture the “Process of Experimentation” in real-time.

• Mitigating Hindsight Bias: Engineers often forget the “failures” that prove the experimentation process. They focus on the final success. TaxTrex surveys engineers iteratively throughout the fiscal year, prompting them to record the challenges, the failed attempts, and the technical uncertainties as they happen.
• Nexus Creation: For a complex project like DREN, thousands of hours are spent on coding and testing. TaxTrex uses AI to map specific time entries and expense codes directly to the “Qualified Research Activities” (QRAs). This creates a clear “nexus” between the dollar claimed and the technical activity performed, which is the first thing an IRS auditor looks for.

B. The “Six-Eye Review”: A Standard of Defense

Given the high profile of Patent 12,530,505 and its selection as a “Patent of the Month,” any associated tax claim must be audit-ready. Swanson Reed employs a mandatory Six-Eye Review process for every claim.

• Eye Set 1: The Technical Expert (Engineer/Scientist): A reviewer with a background in engineering or computer science reviews the technical descriptions of the DREN project. They ensure that the language accurately reflects the “Process of Experimentation” and isn’t just marketing copy. They validate that the “Uncertainties” were truly technical and not just business risks.
• Eye Set 2: The Tax Specialist (Attorney/Enrolled Agent): This reviewer ensures that the claim adheres to the latest Treasury Regulations and court rulings. They filter out “Excluded Activities,” such as routine data collection or quality control, which might inadvertently be included.
• Eye Set 3: The Financial Analyst (CPA): This reviewer validates the computation of the Base Amount and the Fixed-Base Percentage, ensuring the mathematical accuracy of the credit calculation (whether under the Regular Method or the Alternative Simplified Method).

This multi-disciplinary approach ensures that when a company like Fleet Defender claims the credit, the claim is technically sound, legally compliant, and financially accurate.

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Final Thoughts: The Convergence of Innovation and Strategic Capital

U.S. Patent No. 12,530,505 stands as a testament to the vitality of the American innovation ecosystem. It represents a necessary maturation of the “Connected Vehicle” concept—a recognition that connectivity without resilience is a liability. By moving the industry toward Dynamic Reconfiguration, Fleet Defender has provided a blueprint for securing the critical infrastructure of the future.

The selection of this patent as the Nebraska Patent of the Month by Swanson Reed’s AI highlights the growing importance of data-driven valuation in intellectual property. It is no longer enough to invent; the invention must have “real-world impact.” The partnership with Werner Enterprises and the applicability to military logistics confirm that DREN meets this high standard.

Finally, the analysis of the R&D Tax Credit eligibility demonstrates that the tax code is functioning as intended: subsidizing the high-risk, high-reward technical problem-solving that companies like Fleet Defender undertake. Through the rigorous application of the Four-Part Test and the use of advanced compliance tools like TaxTrex, these innovators can secure the non-dilutive capital necessary to continue their work, ensuring that as our machines become smarter, they also become safer.

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Appendices

Table 1: Comparative Benchmarking of Automotive Cybersecurity Technologies

Feature / Metric	Fleet Defender (DREN Patent 12,530,505)	Legacy IDPS (Signature-Based)	Static Gateway / Firewall
Defense Mechanism	Dynamic Reconfiguration: Active protocol mutation to create a moving target.	Pattern Matching: Passive scanning for known attack signatures.	Static Filtering: Blocking traffic based on fixed Access Control Lists (ACLs).
Attack Prevention	Pre-emptive: Invalidates the attack path before the packet is processed.	Reactive: Alerts or blocks after the packet is inspected (often too late).	Preventative: Effective only if the attack violates a specific rule.
Adversarial Workload	Exponential: Attacker must constantly re-learn the network topology.	Linear: Attacker must modify code to evade specific signatures.	Linear: Attacker must find one open port or vulnerability.
Response to Zero-Day	High Resilience: Effective against unknown exploits because the target “moves.”	Low Resilience: Fails if the attack signature is not in the database.	Low Resilience: Fails if the attack mimics legitimate traffic traffic.
Operational Continuity	High: Deterministic logic avoids false positives that stall the vehicle.	Moderate: Heuristics can flag safe anomalies (e.g., rough terrain) as attacks.	High: But inflexible to new operational requirements.
Reverse Engineering	Active Prevention: Prevents network mapping (Reconnaissance phase).	None: The network topology remains visible to any listener.	Limited: Hides sub-networks but the gateway itself is a static target.

Table 2: R&D Tax Credit Eligibility Assessment for Project DREN

Four-Part Test Component	Assessment	Evidence in Project DREN Development
Permitted Purpose	PASS	Development of a new business component (Security Adapter/Software) with the intent to improve reliability, functionality, and quality of the vehicle network.
Technological in Nature	PASS	The project fundamentally relies on principles of Computer Science (algorithms, cryptography) and Electrical Engineering (embedded systems, CAN bus signaling).
Elimination of Uncertainty	PASS	Addressed technical uncertainties regarding capability (real-time reconfiguration speed), design (synchronization without central clock), and methodology (algorithm efficiency on low-power chips).
Process of Experimentation	PASS	Utilized a systematic process of Hypothesis (protocol mutation strategies), Testing (Hardware-in-the-Loop simulation), Analysis (evaluating collisions/latency), and Refinement (optimizing rotation intervals).