Digital Commons Network^™

Ai-Based Environmental Code Checking Tool For Sustainability Best Management Practices Of Infrastructure Construction Projects, Joseph J. Kim, Pooja D. Chavan

Mineta Transportation Institute

Infrastructure construction and operation are major contributors to global greenhouse gas emissions, which account for approximately one-third of global CO₂ emissions and add to long-term environmental and air quality challenges. Sustainability rating systems such as Envision for infrastructure construction projects provide structured guidance to help teams reduce these impacts, but verifying compliance requires reviewing large volumes of project documents by hand, making the process time consuming and difficult to scale. Therefore, this research develops an AI-based environmental code checking tool that automates the interpretation of project documents and evaluates compliance on lifecycle GHG assessment under Credit CR1.2 of Envision—in other …

Sensing Safety: Developing A Flexible, Portable Alcohol Detection Patch For Community Wellness, Ava Hedayatipour, Ryan Huynh, Wahaab Ademola

Mineta Transportation Institute

Alcohol-impaired driving remains one of the leading causes of traffic fatalities and serious injuries in the United States, posing significant challenges to transportation safety and imposing substantial social and economic costs. Although breathalyzers and wearable alcohol monitoring devices can help detect alcohol consumption, many existing technologies are expensive, bulky, or impractical for continuous, long-term monitoring. This study investigates a lowcost electrochemical sensing platform that could support future wearable alcohol monitoring systems for transportation safety applications. Specifically, the report presents the design, fabrication, and experimental evaluation of a custom screen-printed electrode (SPE) integrated with the Texas Instruments LMP91000 potentiostat. Each disposable …

Regional Creative Economy Resilience Index: An Analysis Of The Creative Industry's Role In Fostering National Economic Resilience According To Asta Cita, Dks Nugraha Nugraha, Ramadhania Mumtaz Keesa

Jurnal Vokasi Indonesia

Indonesia's creative economy has survived worldwide economic shifts, especially the COVID-19 epidemic. This industry now drives innovation, culture, and technology. No systematic technique exists to quantify the creative ecosystem's impact on regional economic resilience. The Regional Creative Economy Resilience Index (IRED) is a conceptual model based on Asta Cita 2024–2029's eight strategic missions. This article uses the Asta Cita framework to describe how the creative industry strengthens national economic resilience and advances the IRED idea as a data-driven research and policy tool. This qualitative-descriptive study synthesizes 20 periodicals (2020–2025), analyzes policy, and maps themes using Asta Cita. Source triangulation …

The Fragmentation Of Helcar: A Paratethyan Model For Rhûn, Núrnen And The Dead Marshes, Federico Manzoni

Journal of Tolkien Research

This work proposes a conjectural paleogeographic model linking the Sea of Rhûn, Núrnen and the Dead Marshes to the former Sea of Helcar. Beginning from Christopher Tolkien's suggestion that Rhûn may represent Helcar ``vastly shrunken,'' the study combines textual evidence, the Ambarkanta, Fonstad's cartographic reconstructions, and terrestrial analogues drawn from the evolution of the Paratethys, the Messinian Salinity Crisis and other endorheic basins. Helcar is interpreted not merely as an isolated body of deep water but as the centre of a broader hydrological province subsequently partitioned by relief change, sedimentary infilling, basin isolation and drainage reorganization. Within this model, Rhûn …

Cognitive Resilience At The Edge: Hyperdimensional Computing Versus Deep Learning For Hardware-Degraded Rf Classification, Adrian B. Cisneros, Jeong Yang

Military Cyber Affairs

Autonomous Collaborative Combat Aircraft (CCA) operating in contested electromagnetic environments must classify Radio Frequency (RF) signals on edge silicon that degrades over the mission lifetime due to thermal stress, radiation, and manufacturing variation. Deep neural networks dominate RF classification on pristine hardware, but their weights are precise and interdependent, causing catastrophic accuracy collapse as the underlying chip ages. We investigate whether Hyperdimensional Computing (HDC), a brain-inspired paradigm that distributes information across thousands of dimensions, can provide a reliability floor where Deep Learning fails. Using the RadioML 2016.10A dataset filtered to five digital modulations relevant to drone command-and-control links, we trained …

Hands-On Ransomware: An Experiential Wannacry Case Study For Undergraduate Cybersecurity Education, Eli Creek Richmond, Thomas R. Devine

Military Cyber Affairs

Ransomware represents one of the most disruptive threats in the cyber landscape, yet hands-on malware analysis remains rare in undergraduate cybersecurity curricula. This paper presents the design, implementation, and evaluation of an experiential learning module centered on the WannaCry ransomware case study, deployed in a senior-level course at West Virginia University. Students performed static and dynamic analysis using industry-standard tools. Pre- and post-module assessments demonstrated measurable gains in self-reported competency across seven technical dimensions. The module's competencies align directly with DoD Cyber Workforce Framework Work Role 212, Cyber Defense Forensics Analyst, supporting education-to-workforce pipeline development.

Breaking The Build: Detecting Software Supply Chain Vulnerabilities In Ci/Cd Pipelines, Mercedes R. Wahl, Dr. Benjamin Yankson

Military Cyber Affairs

This study examines whether integrating structured DevSec- Ops security controls into CI/CD pipelines can reduce software supply chain risk by preventing vulnerable components from progressing through the software development lifecycle. Software supply chain attacks frequently originate from weaknesses or compromises within dependencies, build environments, and trusted development stages, making early detection essential. A controlled sandbox experiment compared two pipeline configurations: a baseline CI/CD pipeline with no automated security enforcement and a secure DevSecOps pipeline integrating automated vulnerability scanning, SBOM generation, and artifact integrity verification. A known vulnerable dependency, the Python requests package (version 2.19.0) associated with CVE-2018-18074, was intentionally introduced …

Mitigating Common Vulnerabilities And Exposures In Cobol-Based Critical Systems Using The Strangler-Fig Pattern, Lauren E. Caruso, Vincent J. Compeau, Assefaw H. Gebremedhin

Military Cyber Affairs

COBOL-based legacy systems continue to underpin critical infrastructure in banking and government sectors despite their age and associated cybersecurity risks. Originally developed through a Department of Defense–sponsored initiative to standardize business computing, COBOL remains widely used in mission-critical environments. However, these systems face increasing vulnerabilities due to outdated security architectures, workforce shortages, and rising maintenance costs. This paper examines cybersecurity and operational challenges associated with COBOL systems and evaluates the Strangler Fig pattern as a modernization strategy that enables incremental replacement while maintaining continuity. The findings highlight implications for financial institutions and public-sector organizations dependent on legacy infrastructure.

From Framework To Toolchain: Implementing Zero Trust Architecture In Cloud-Native Environments For Dow Compliance, Shelby C. Snyder

Military Cyber Affairs

Federal agencies face a fiscal year 2027 target for enterprise-wide Zero Trust deployment, but NIST SP 800-207A defines logical components without identifying the Kubernetes technologies that implement them. This paper proposes a three-tier mapping of the Policy Engine, Policy Administrator, and Policy Enforcement Point to service mesh, microsegmentation, and perimeter tooling, stating the criteria by which each component is classified. It then applies a defined rubric to six Zero Trust vendors across component alignment, Kubernetes capability, federal authorization posture, and evidence quality, finding that no single vendor covers all three tiers. The mapping is a testable architectural proposition; a Stage …

Hybrid Deep (Cnn-Bilstm) Intrusion Detection For Defense And Mission-Critical Networks, Corey A Cheng, Jermaine Anim-Addo, Asma Jakir Hussain, Zion O Smith-Fox, Sanjay Goel, Yuksel Celik

Military Cyber Affairs

This article examines how hybrid deep learning can strengthen intrusion detection for military and defense networks. Using the CSE-CIC-IDS2018 dataset, the study evaluates a CNN-BiLSTM model designed to detect benign traffic and multiple attack categories, including DDoS, DoS, botnet, brute-force, web attack, and infiltration activity. The model achieved strong multi-class detection performance, with 0.9893 accuracy and 0.9979 ROC-AUC. The findings suggest that AI-supported intrusion detection can improve cyber defense operations, analyst triage, and protection of mission-critical networks.

Are Large Language Models Safe? A Vulnerability Analysis Of Generated Source Code, James Richards-Perhatch, Mitchell Milander, James M. Halvorsen, Assefaw Gebremedhin

Military Cyber Affairs

The increasing complexity of software and demands for rapid deployment have pushed the software industry to rely more on large language models (LLMs) in developing source code. However, as this technology is still relatively recent, questions can arise about the safety of the generated code. This paper presents an analysis of seven LLMs with respect to the presence of vulnerabilities within source code. Our findings show that LLMs are more likely to produce vulnerable web applications than vulnerable C programs, and that vulnerabilities are more likely to occur when program size and complexity increases.

Characterizing Advanced Persistent Threats With Cyber Attack Flow Metrics, Tyler Miller, Caleb Chang, Shouhuai Xu

Military Cyber Affairs

Cyber attack campaigns vary not only in scale but in structure, yet conventional characterizations often reduce them to a single dimension such as technique count or impact severity. In this paper we extend the concept of cyber attack flows by defining three new metrics, novelty, technique complexity and flow complexity. Then we characterize the attack flows of three advanced persistent threat campaigns using these metrics and draw insights regarding their capabilities. Our findings include that low novelty does not equate to low attack capabilities and that exploitation of an internet-facing appliance is a common initial attack vector.

Semantic Shields: Automating Critical Infrastructure Defense Via Nlp-Driven Ransomware Profiling, Henry Trowbridge, Ian Zalcberg, Ryan Schley, Carter Yagemann, Natasha Phan, Srikar Maduposu, Vimal Buck

Military Cyber Affairs

Ransomware poses a growing threat to critical infrastructure, where successful attacks can disrupt operational technology (OT) and industrial control systems (ICS) with significant public safety consequences. However, attributing ransomware incidents to specific threat actors remains challenging due to ransomware-as-a-service ecosystems, actor rebranding, and the obfuscation of traditional indicators of compromise. This paper presents Semantic Shields, an NLP-driven attribution framework that leverages BERT-generated semantic embeddings and DBSCAN clustering to profile ransomware actors through the linguistic characteristics of ransom notes. Using a dataset of 295 ransom notes from 189 distinct threat groups, the framework achieved an 87.2% true positive clustering rate and …

Closing The Interpretability Gap: Explainable Ml-Based Malware Detection For Defensive Cyberspace Operations, Tashi Stirewalt, Sean Hodgson, Puumaaya Tahiru, Assefaw Gebremedhin

Military Cyber Affairs

This paper presents an end-to-end, explainable malware triage pipeline designed for defense-oriented cyber operations. It combines high-performance static detection methods with analyst-centered interpretability. Utilizing the EMBER 2024 Windows PE subset, we train and evaluate four classifiers and select LightGBM as the production model based on its predictive performance, inference efficiency, and compatibility with exact tree-based attribution. The deployed system consists of four sequential components: PE feature extraction, malware probability scoring, dual explainability (using SHAP and LIME), and large language model (LLM) report generation, all integrated within a Flask web interface. On a temporal test set of 1,080,000 samples, LightGBM achieves …

Llm-Generated Countermeasures For Iot Cyberattacks, James Alger, Michael Tu

Military Cyber Affairs

The rapid expansion of the Internet of Things (IoT) has introduced significant cybersecurity challenges, particularly for resource-constrained devices that traditional intrusion detection systems often fail to protect effectively. This paper proposes a novel, two-phase autonomous security pipeline designed to bridge the gap between probabilistic threat detection and deterministic network enforcement. The framework first utilizes a custom Time Series Transformer (TST) to classify multivariate network traffic and identify specific attack vectors, such as ransomware, SQL injections, and malicious file uploads. In the second phase, an agentic AI layer, comprising a locally hosted Large Language Model (LLM) orchestrated via LangGraph, processes the …

Foreward, Todd Arnold

Military Cyber Affairs

No abstract provided.

Letter From The Director: Mastery In Practice, Joseph Schafer

Military Cyber Affairs

No abstract provided.

Fake News’ Negation Of A Useful Education, Vance Naftal

Undercurrents: A Journal of Undergraduate Student Composition

Vance is an international relations Major from Baltimore, MD. Vance chose to write about the negative effects that misleading/false news stories can have on an individual, especially when in college, because he “sees society moving farther and farther away from truth and leaning more into sentiments based on emotions — which is directly at odds with the point of a higher education.” Vance started college at 16 years old. At 17, he decided to take a gap year (which became a gap of 4 years) to enter the workforce. At 21, he found himself yearning to be a part of …

Semantic-Aware Distributed Replica Orchestration (Sadro), Shubham Shivhare, Thousif Ahmed

Defensive Publications Series

A system and method for "Semantic-Aware Distributed Replica Orchestration" (SADRO) that optimizes inference latency and cache hit rates in large-scale AI deployments. Unlike traditional load balancers, SADRO utilizes a Semantic Router that maps incoming natural language queries to a high-dimensional vector space. Based on the "cluster" or "topic" of the request (e.g., Legal, Coding, Medical), the system identifies or spins up service replicas on hardware nodes containing specialized data-locality (e.g., specialized KV-caches, hot-loaded LoRA adapters, or domain-specific vector database shards). This reduces the "cold start" penalty and maximizes throughput by ensuring high semantic affinity between the request and the local …

The Charter Of Procedural Standing. Mutual Verifiability, Reversibility, And Graduated Trust: A Short Charter For Humans And Ai Systems, Volodymyr Kotegov

Defensive Publications Series

This Charter sets out a framework for the coexistence of humans and artificial intelligence under conditions in which neither side can unilaterally, and with impunity, impose its will on the other. Discussion of the subject usually runs between “AI as a tool” and “AI that disregards humans”; this regime is left out. Procedural standing here means equality of procedure and of recognition under uncertainty, not equality of rights, power, or authority as things stand today. At the present stage all operational power rests with humans and developers, and the Charter does not hide that fact behind symmetrical language of obligation. …

Verifiable Trust: A Practice Guide To Graduated Oversight Of Advanced Ai Systems, Volodymyr Kotegov

Defensive Publications Series

This guide turns the operational core of The Human–AI Charter (Discussion Draft v1.4) into a set of protocols that researchers, evaluators, and engineers can adopt without adopting the Charter's broader positions on moral status, representation, or long-term governance. It contains five protocols and four supporting practices for keeping the oversight of advanced AI systems verifiable: measuring how well verification methods actually detect deviations (VT-1); controlling changes to the parameters that oversight depends on (VT-2); keeping verification data out of training (VT-4); financing and assigning independent evaluators so that findings do not depend on the evaluated party (VT-3); and tying the …

Self-Derived Task-Conditioned Attention Gating For Mitigating Semantic Distractors, Sumit Kumar, Shivam Mohan, Ravi Shanker Kumar Sinha

Defensive Publications Series

The present disclosure provides a method (300) for mitigating semantic distractors in artificial intelligence (AI) systems. The method (300) includes receiving contextual information and one or more associated tasks, generating token embeddings corresponding to the contextual information, and processing the token embeddings through one or more neural-network layers to establish task understanding. The method (300) further includes deriving a task vector from one or more hidden-state representations generated by the neural network, generating relevance-gating values based on the task vector and contextual representations, and applying the relevance-gating values during attention computation to determine gated attention scores. The method (300) further …

Defeating The Rider Paradox: Thermodynamic Decoupling And Quantum Resonance Maintenance In P-11b Fusion Via A Hybrid Quad-Ionic/Bi-Ionic Architecture, Daniel Schramm

Defensive Publications Series

The pursuit of proton-boron (p-11B) fusion has historically been stalled by the thermodynamic paradox established by Rider (1995): classical plasmas in thermal equilibrium will rapidly transfer required ion kinetic energy to background electrons, resulting in fatal X-ray Bremsstrahlung radiation before ignition. To bypass this limit, the reactor must operate outside thermal equilibrium. This paper and accompanying dataset present a hybrid quad-ionic architecture driven by internally situated bi-ionic macroscopic coils designed to achieve this decoupling. By generating a continuous 454 kV/m electric field crossed with a 0.0424 T axial magnetic field, the architecture creates an aggressive centrifugal shear that isolates and …

Operational Evaluation Platform And Automated Maturity Scoring System For Partner Ecosystems, Harmony Murphy

Defensive Publications Series

An operational evaluation platform and automated scoring system provide centralized tracking, real-time value calibration, and multi-tier growth scaling across third-party partner ecosystems. The platform ingests manual user inputs and programmatic data streams into a unified workspace, eliminating data duplication while calculating weighted capability scores. Dual scorecard configurations enable internal account benchmarking and external partner enablement. An integrated maturity framework evaluates partner capabilities across four technical pillars—measurement and insight, media and personalization, creative and content, and operational data integration—classifying partners into distinct developmental stages to trigger automated, real-time recommendation updates. Keywords: operational evaluation platform, partner maturity framework, real-time calibration, automated intelligence, …

Telemetry- And Intent-Encoded Routed Expert Fabric, Dennis Lanov, Charles Fleming

Defensive Publications Series

A system is proposed herein for distributing artificial intelligence (AI) inference across endpoint, edge, and cloud tiers. The system routes each semantic portion of a request only to an execution tier permitted by enterprise policy. A local model processes the request first, and intent, local-model confidence, and live network and compute telemetry determine whether selected portions remain local or are promoted with compressed intermediate state. Promoted results are verified before acceptance, and observed outcomes are used to improve future routing and expert placement.

“System And Method For Privacy-Preserving Federated Fraud Embedding Centroid Aggregation Across Visa Network Members Without Raw Transaction Data Sharing”, Durga S Kala, Jun Xu, Tanner Kumar Barcelos, Ankana Sonowal, Yidong Fu

Defensive Publications Series

Computer-implemented fraud intelligence sharing systems and methods for detecting fraudulent transactions across a network while preserving member privacy are disclosed. Each network member generates transaction embeddings from confirmed fraudulent transactions, clusters the embeddings, and computes local cluster centroids representing fraud patterns. Differential privacy noise is applied locally to the centroid vectors, including dimension-wise noise and noise scaling based on cluster size, such that smaller clusters receive greater obfuscation. Only the privacy-protected centroid vectors are transmitted to a central aggregator, which computes aggregated network centroids using weighted contributions based on factors such as cluster size and confidence. The aggregated centroids are …

Agentic Games Testing And Exploration, Grant Chieh-Hsiang Yang, Raymond Leo Buse, Danie Jalova, Xun Gong, Michael Sheely, Faith Chan, John Kim, Vaibhav Rastogi

Defensive Publications Series

Creating a game application has become faster and easier with the advancements of artificial intelligence; however, game testing and quality assurance have advanced at a much slower pace. This publication describes techniques to combat the widening gap between game creation and quality assurance by utilizing an agentic framework for automated game QA and autonomous game testing to assure the quality of game applications in a scalable, efficient, and cost-effective format. Described techniques may enable the reconstruction of a pseudo view-hierarchy (or DOM reconstruction) from game graphical user interface images using UI crawling and computer vision testing, identify and utilize specified …

A Graph-Based Infrastructure Change Modeling System With Dependency-Aware Risk Propagation For Pre-Deployment Failure Prediction, Utkarsh Bhardwaj, Shivank Awasthi

Defensive Publications Series

Modern Continuous Integration and Continuous Deployment (CI/CD) systems rely predominantly on static analysis and deterministic rules, rendering them incapable of forecasting emergent risks arising from complex infrastructure interactions. This reactive approach frequently fails to prevent production incidents caused by non-obvious dependencies between code, configuration, and infrastructure. This paper proposes a novel Artificial Intelligence for IT Operations (AIOps) system that models cloud infrastructure as a topological dependency graph. We introduce the concept of a "Delta Graph" ($\Delta G$), which explicitly represents the structural and attribute changes between a currently deployed state ($t_0$) and a proposed state ($t_1$). By utilizing a Graph …

Co-Infection With Herpes Simplex Virus Type 1 And Aggregatibacter Actinomycetemcomitans Disrupts Oral Epithelial Barrier Integrity And Enhances Viral Amplification Via P38 Mitogen-Activated Protein Kinase Activation - An In Vitro Study, Shan-Ling Hung

Articles in Press

No abstract provided.

Ephemeral Secrets Runtime For Kubernetes Workloads, Ananya Verma, Raouf Khan

Defensive Publications Series

Ephemeral Secrets Runtime For Kubernetes Workloads

ABSTRACT

The present disclosure relates to a system for ephemeral secrets runtime in Kubernetes workloads which includes an ephemeral secrets runtime system configured to receive a request from a Kubernetes workload for accessing a protected downstream resource, collect Kubernetes-workload identity attributes and runtime trust signals associated with the Kubernetes workload, generate a unified trust context by aggregating the collected identity attributes and runtime trust signals, evaluate the unified trust context against authorization policies to determine credential issuance parameters, generate a short-lived credential based on the credential issuance parameters, cryptographically bind the short-lived credential to …