SUNY New Paltz
University of Oxford
Louisiana State University and Agricultural and Mechanical College
University of Colorado Colorado Springs
Southern Methodist University
Clemson University
San Jose State University
New Jersey Institute of Technology
Pace University - New York
Singapore Management University
The Algorithmic Narcissus: Ai Validation And The Atrophy Of The 'Athletic' Social Self,
2026
University of Queensland
The Algorithmic Narcissus: Ai Validation And The Atrophy Of The 'Athletic' Social Self, Ilham Phalosa Reswara, Anggi Mayangsari
Jurnal Psikologi Sosial
Artificial intelligence systems designed around continuous affirmation and minimal friction are becoming increasingly prominent as social and relational partners in everyday life. This article examines how sustained interaction with such frictionless AI systems may reshape the developmental conditions under which social selfhood forms and is maintained. Drawing on Cooley's (1902) looking glass self, Kohut's (1971) concept of optimal frustration, and the developmental literature on social competence, the article argues that frictionless AI may distort the social mirror through which identity forms, remove the manageable frustration that appears necessary for psychological growth, weaken empathic capacity, and erode the social stakes that …
Cognitive Resilience At The Edge: Hyperdimensional Computing Versus Deep Learning For Hardware-Degraded Rf Classification,
2026
Texas A&M University-San Antonio
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 …
Breaking The Build: Detecting Software Supply Chain Vulnerabilities In Ci/Cd Pipelines,
2026
SUNY Albany
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 …
From Framework To Toolchain: Implementing Zero Trust Architecture In Cloud-Native Environments For Dow Compliance,
2026
Northeastern University
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,
2026
University at Albany, SUNY
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,
2026
Washington State University
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.
Semantic Shields: Automating Critical Infrastructure Defense Via Nlp-Driven Ransomware Profiling,
2026
The Ohio State University
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 …
Llm-Generated Countermeasures For Iot Cyberattacks,
2026
Purdue University Northwest
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,
2026
Military Cyber Affairs
Letter From The Director: Mastery In Practice,
2026
Military Cyber Institute
Letter From The Director: Mastery In Practice, Joseph Schafer
Military Cyber Affairs
No abstract provided.
Saas As The Backbone Of Digital Transformation: How Ai Turned Cloud Software Into Intelligent Enterprise Infrastructure,
2026
International Burch University
Saas As The Backbone Of Digital Transformation: How Ai Turned Cloud Software Into Intelligent Enterprise Infrastructure, Amar Fejzić
Communications of the IIMA
Software-as-a-Service (SaaS) has become one of the most consequential infrastructures of digital transformation because it lowers the cost, time, and complexity of adopting enterprise capabilities. At the same time, artificial intelligence (AI), especially generative and conversational AI, is changing SaaS from a delivery model into an intelligent operating layer that automates workflows, personalizes customer interactions, and supports data-driven decisions. This paper develops a conceptual synthesis of academic literature and public organizational cases to examine how SaaS shaped digital transformation and how AI is reshaping SaaS itself. The analysis shows that SaaS enables scalable experimentation, faster deployment, and modular integration, while …
How Do Governments Combat Jihad Online?,
2026
University of Massachusetts Boston
How Do Governments Combat Jihad Online?, Noah Cook
Honors College Theses
Since the September 11th, 2001 attacks, jihadist groups like ISIS, al-Qaeda, and al-Shabaab have used the internet as a tool for recruitment, radicalization, and propaganda dissemination. Since then, governments, non-governmental organizations, and private companies have developed strategies to combat jihadism on the internet. This paper examines how these actors have worked to mitigate the digital jihadist footprint and its effects by analyzing three main approaches to countering violent Islamist extremism on the internet: counter narrative initiatives, awareness-raising programs, and content removals. This paper finds that each of these methods has its own strengths and weaknesses. Counter narrative campaigns show the …
Using Clustering Techniques And Mitre Att&Ck Threat Interpretation To Detect Anomalies In Modbus/Tcp For Industrial Control Systems,
2026
Department of Electrical & Computer Engineering and Computer Science, University of New Haven
Using Clustering Techniques And Mitre Att&Ck Threat Interpretation To Detect Anomalies In Modbus/Tcp For Industrial Control Systems, Shivanjali Khare, Tirthankar Ghosh, Jhansi Sreya Jagarapu, Atharva Haridas Sagare
Journal of Cybersecurity Education, Research and Practice
Recent attacks on America's critical infrastructure have drawn increased attention on securing industrial control systems and operational technology in power plants, utility companies, and other sectors providing public services. Attack detection and mitigation strategies on these systems have shown promising results using machine learning and other statistical baselining techniques, mostly using supervised learning and classification. Unsupervised learning using cluster analysis and other techniques remain mostly unexplored. In this paper, we propose multi-layered feature extraction and hybrid clustering framework to detect fine-grained nested attack patterns in Modbus-over-TCP traffic. Operating under the assumption of known number of distinct network categories, our approach …
Radaround: A Field-Expedient Direction Finder For Contested Iot Sensing & Em Situational Awareness,
2026
Embry-Riddle Aeronautical University
Radaround: A Field-Expedient Direction Finder For Contested Iot Sensing & Em Situational Awareness, Owen Maute, Blake Roberts
Discovery Day - Daytona Beach
This paper presents RadAround, a passive 2-D direction-finding system designed for adversarial IoT sensing in contested environments. Using mechanically steered narrowbeam antennas and field-deployable SCADA software, it generates high-resolution electromagnetic (EM) heatmaps using low-cost COTS or 3D-printed components. The microcontroller-deployable SCADA coordinates antenna positioning and SDR sampling in real time for resilient, on-site operation. Its modular design enables rapid adaptation for applications such as EMC testing in disaster-response deployments, battlefield spectrum monitoring, electronic intrusion detection, and tactical EM situational awareness (EMSA). Experiments show RadAround detecting computing machinery through walls, assessing utilization, and pinpointing EM interference (EMI) leakage sources from Faraday …
Remora Ads-B In Receiver,
2026
Embry-Riddle Aeronautical University
Remora Ads-B In Receiver, Noah Evans, Sara Patel, Nicholas Gatto, Daniel Ingleton
Discovery Day - Daytona Beach
The Remora is a compact Automatic Dependent Surveillance Broadcast (ADS-B) receiver, designed to enhance situational awareness for pilots operating experimental and homebuilt aircraft. Unlike conventional panel or windscreen-mounted systems, the Remora is installed externally, making it a low-profile solution to the ADS-B In challenge. The device integrates easily with the aircraft’s existing power supply. It transmits data wirelessly to electronic flight bags (EFBs) within the cockpit, using existing iOS and Android software for display and user interaction. The primary function of the Remora is to provide public-access, real-time traffic awareness and weather forecasting, enabling pilots to make informed mission decisions. …
Balancing Innovation And Integrity: Ethical Implications Of Ai Use In Higher Education,
2026
Women's University in Africa
Balancing Innovation And Integrity: Ethical Implications Of Ai Use In Higher Education, Petronella Muziti, Ntando S. Sibanda
African Conference on Information Systems and Technology
Artificial intelligence (AI) is rapidly transforming higher education, creating opportunities while raising ethical concerns such as bias, privacy violations, academic misconduct and reduced human-centred teaching. This study examined how institutions can balance AI innovation with academic integrity and equity, focusing on lecturers’ ethical dilemmas and institutional readiness. A triangulation design incorporating quantitative and qualitative methods was used. Twelve semi-structured interviews and two focus group discussions involving eighteen participants were conducted across five universities, while 150 questionnaires were distributed, yielding 128 valid responses. Descriptive statistics and thematic analysis were applied. Findings showed that lecturers reported student misuse and plagiarism (78%), AI …
Ubuntu Signs: A Tool For Real-Time Translation Of South African Sign Language,
2026
Nelson Mandela University
Ubuntu Signs: A Tool For Real-Time Translation Of South African Sign Language, Simone Beets, Hasita Rama
African Conference on Information Systems and Technology
Deaf and Hard-of-Hearing (DHH) individuals often face communication challenges with people who do not understand sign language. Existing systems lack support for South African Sign Language (SASL) and are limited in accessibility, efficiency and accuracy. This paper introduces Ubuntu Signs, a mobile prototype tool for real-time SASL translation using a camera-based interface and machine learning. The tool uses two models to recognize gestures: a static model for alphabetical and numerical gestures and a dynamic model for continuous gestures representing words and phrases. A preliminary user study evaluated the effectiveness, efficiency, accuracy and satisfaction of Ubuntu Signs. Results indicated high usability …
Robbed With Permission: Toward A Receiver-Consent Architecture For Trust-Preserving Mobile Money Transactions In Africa,
2026
University of Ghana Business School
Robbed With Permission: Toward A Receiver-Consent Architecture For Trust-Preserving Mobile Money Transactions In Africa, Raphael Amponsah
African Conference on Information Systems and Technology
Mobile money transacted more than USD 2 trillion globally in 2025 across 2.3 billion registered accounts, with Sub-Saharan Africa holding over half of all accounts and two-thirds of transaction value. Between 58 and 72 percent of mobile money fraud on the continent exploits trust rather than technology, yet current security architecture authenticates the sender and merely notifies the receiver, leaving the transaction execution moment structurally unguarded. This paper introduces the Receiver-Consent Transaction Protocol (RCTP), a USSD-native protocol requiring the receiver's active PIN consent on their physical SIM before any credit completes, grounded in Design Science Research with Routine Activity Theory …
Physics-Guided Deep Learning For Predictive Modeling Of Spatiotemporal Dynamical Systems,
2026
Old Dominion University
Physics-Guided Deep Learning For Predictive Modeling Of Spatiotemporal Dynamical Systems, Niharika Deshpande
Engineering Management & Systems Engineering Theses & Dissertations
Many physical and networked systems evolve under continuously changing spatial and temporal conditions. Transportation networks respond to fluctuating demand, atmospheric fields reorganize as storms intensify, and coastal response depends on localized forcing pathways. Modeling such systems requires learning formulations that adapt to evolving structure, operate on irregular geometries, and provide interpretable measures of predictive uncertainty. This dissertation develops a physics-guided spatiotemporal learning framework designed for structured dynamical systems whose governing interactions are neither static nor Euclidean. The central premise is that spatial relationships in these systems are dynamic and geometry-dependent. To represent this behavior, system states are modeled on time-varying …
Evaluating Machine Learning Models On Classification Of Novel Cyber Attacks In The Healthcare Domain,
2026
Minnesota State University Moorhead
Evaluating Machine Learning Models On Classification Of Novel Cyber Attacks In The Healthcare Domain, Promise Ehimen
Dissertations, Theses, and Projects
The increasing adoption of the Internet of Medical Things (IoMT) has improved healthcare delivery through connected medical devices while simultaneously expanding the cybersecurity risks facing healthcare organizations. Although machine learning based intrusion detection systems have demonstrated high detection accuracy, their ability to respond reliably to previously unseen cyberattacks remains uncertain. This study investigated how a Neural Network model and a Logistic Regression model classified novel cyberattacks within the IoMT environment. The Neural Network and Logistic Regression models were both trained and tested using a subset of the CICIoMT2024 benchmark dataset. The Neural Network achieved 99.82% test accuracy and a 0.94 …
