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Bloch oscillation with a diatomic tight-binding model on quantum computers
Get PDFWe aim to explore a more efficient way to simulate few-body dynamics on quantum computers. Instead of mapping the second quantization of the system Hamiltonian to qubit Pauli gate representation via the Jordan-Wigner transform, we propose to use the few-body Hamiltonian matrix under the state-vector basis representation, which is more economical on the required number of quantum registers. For a single-particle excitation state on a one-dimensional chain, Γ qubits can simulate =2^Γ number of sites, in comparison to qubits for sites via the Jordan-Wigner approach. A two-band diatomic tight-binding model is used to demonstrate the effectiveness of the state-vector basis representation. Both one-particle and two-particle quantum circuits are constructed, and some numerical tests on IBM hardware are presented
Fine Grained Address Space Layout Randomization Using Noncontiguous Per-Function Memory Segment Assignment
No full textAddress Space Layout Randomization (ASLR) is a popular exploit mitigation provided by most modern operating systems today. It works by randomizing the base address of position-independent code and data segments loaded in memory at run-time to make it more difficult for an attacker to guess their locations. This design choice makes ASLR fast and efficient, but suffers from a major flaw: If an attacker can leak any pointer to the randomized memory mapping, they can easily calculate the corresponding base address, and in turn calculate the address of any other code or data in that segment, rendering the protection mechanism entirely useless. This paper proposes a novel Fine Grained Address Space Layout Randomization (FG-ASLR) solution utilizing noncontiguous per-function memory segment assignment. This allows code to be randomized at the function level quickly and efficiently, leveraging existing operating system mechanisms, and enhancing security without significantly impacting system performance. The evaluation shows that FG-ASLR using noncontiguous per-function memory assignment is indeed possible for real-world software, does not pose an insurmountable performance impact in load-time, run-time, memory usage, or disk usage, and indeed enhances the security of the programs to which it is applied
Switching Wearable Fitness Devices: Exploring Factors in Switching
Get PDFWearable fitness devices continue to grow in popularity worldwide, with projections indicating 347 million users by 2028. While the adoption and continued use of these devices have been extensively researched, limited investigation has addressed why users switch devices—a critical phase in the Information Systems Discontinuance model. The existing research on Information Systems (IS) switching for wearable fitness devices has primarily explored switching intention rather than examining actual switching behavior. The literature emphasizes the importance of studying actual behavior versus intent, noting they often differ significantly. This qualitative study employed deductive and inductive thematic analysis of Reddit data from users who switched wearable fitness devices. Using the Push-Pull-Mooring theoretical framework as a guide, the analysis revealed ten distinct themes influencing users\u27 decision to switch devices. This research contributes to IS Discontinuance and provides practical insights for WFD manufacturers seeking to understand customer switching behavior
A Mixed Methods Comparative Analysis of Organizational Incident Response to SolarWinds and Log4Shell
Get PDFThis mixed-methods sequential explanatory study investigated how cybersecurity professionals construct meaning during and after major cyber incidents, using the SolarWinds supply-chain attack and Log4Shell vulnerability crisis as paired case studies. Through quantitative surveys (N=31) followed by in-depth qualitative interviews (N=10) with incident responders and leaders, the research revealed systematic perception gaps that fracture along various demographics and roles. Technical staff anchored SolarWinds to its six-month dwell time while executives emphasized procurement timelines; practitioners quantified Log4Shell’s impact through unacknowledged labor hours while those uninvolved in that effort dismissed it as a non-incident. These divergences were not random recall errors but predictable patterns of role-contingent sensemaking.
The study’s key contribution is demonstrating that incident response suffers not just from technical challenges, but from unexamined epistemological divides—different professional communities literally experience different cyber events even when responding to the same attack. Crucially, alignment emerged only around materially implemented solutions (threat hunting teams post-SolarWinds, WAF deployments post-Log4Shell), suggesting organizational learning concretizes through artifacts rather than abstract agreements. These findings compel a paradigm shift in cyber resilience practice: from merely improving detection and response to deliberately building shared interpretation frameworks. The paper concludes with specific design principles for epistemic-aware tools and processes that transform perceptual gaps from vulnerabilities into diagnostic assets
CEKER: A Generalizable LLM Framework for Literature Analysis with a Case Study in Unikernel Security
Get PDFLiterature reviews are a critical component of formulating and justifying new research, but are a manual and often time-consuming process. This research introduces a novel, generalizable approach to literature analysis called CEKER which uses a three-step process to streamline the collection of literature, the extraction of key insights, and the summarized analysis of key trends and gaps. Leveraging Large Language Models (LLMs), this methodology represents a significant shift from traditional manual literature reviews, offering a scalable, flexible, and repeatable approach that can be applied across diverse research domains. A case study on unikernel security illustrates CEKER\u27s ability to generate novel insights validated against previous manual methods. CEKER’s analysis highlighted reduced attack surface as the most prominent theme. Key security gaps included the absence of Address Space Layout Randomization, missing debugging tools, and limited entropy generation, all of which represent important challenges to unikernel security. The study also revealed a reliance on hypervisors as a potential attack vector and emphasized the need for dynamic security adjustments to address real-time threats
Dataset of SCADA traffic captures from a medical waste incinerator with injected cyberattacks
No full textThis paper presents network traffic captures from a Supervisory Control and Data Acquisition (SCADA) system installed in a medical waste incinerator. The dataset comprises 14 daily packet capture files (day01.pcap to day14.pcap), collected over a two-week period from a Siemens S7–1500/ET200MP-based SCADA system. The total number of packets in all files is over 19 million packets. The traffic was captured using Wireshark on the Human-Machine Interface (HMI) terminal directly connected to the Programmable Logic Controller (PLC), recording network communication via the OPC and PROFINET protocols. In addition, eight traffic captures were generated by injecting relevant synthetic cyber-attack traffic into selected daily captures to simulate real-life attack scenarios, including Man-in-the-Middle (MITM), Replay, Packet Fuzzing, Command Flooding, Data Spoofing, Protocol Exploitation, Stealthy Command Injection, and SYN Flooding. Each attack-injected files contains approximately 20,000 attack packets. This dataset is invaluable to support cybersecurity research in industrial control systems (ICS), and it provides a comprehensive resource for analyzing normal SCADA behavior and for evaluating intrusion detection systems (IDS) under various common attack conditions
Employee and Employer Perceptions Regarding Cybersecurity Education During The Hiring Process
Get PDFThe purpose of this phenomenological study was to explore how three major educational avenues of learned skills (no formal education/on-the-job training), cybersecurity certifications, and formal degree pathways impact cybersecurity workforce hiring practices. This research was conducted by using a combination of surveys, online interviews, and job posting analysis. The participants were students at the University of Arizona, a major bank, a large Silicon Valley based software company, and a “big 4” consulting firm. Furthermore, this phenomenological study sought to understand how educational backgrounds are perceived when making hiring decisions. The results were used to develop a composite description of perceptions regarding what and how education avenues impact hiring preferences and practices
AI Super Resolution for Structural Damage Detection From Low Quality Sources
No full textThe identification of damages following natural disasters is of critical importance, as it plays a crucial role in mitigating the risk of subsequent harm, including additional structural damage, injuries, or fatalities. Artificial intelligence (AI) presents significant advantages in this domain by offering faster, more precise, and scalable assessments compared to traditional reliance on expert evaluations alone. When integrated with expert analysis, AI has the potential to enhance the efficiency and accuracy of damage detection, facilitating a comprehensive and rapid assessment of affected structures. Such capabilities are vital for minimizing future risks and enabling timely and effective recovery efforts. This study proposes an AI-based super-resolution method designed to detect structural damages from low-quality data sources. By enhancing the clarity and detail of damage assessments, the proposed approach provides critical information to first responders, enabling them to take informed and calculated measures in disaster response scenarios. This methodology aims to bridge existing gaps in damage detection systems, contributing to improved resilience and preparedness in the face of natural disasters
Factors That Impacted Switching Wearable Fitness Devices
No full textWearable fitness devices continue to grow in popularity worldwide, with projections indicating 347 million users by 2028. While the adoption and continued use of these devices have been extensively researched, limited investigation has addressed why users switch devices—a critical phase in the Information Systems Discontinuance model. The existing research on Information Systems (IS) switching for wearable fitness devices has primarily explored switching intention rather than examining actual switching behavior. The literature emphasizes the importance of studying actual behavior versus intent, noting that they often differ significantly. This qualitative study employed deductive and inductive thematic analysis of Reddit data from users who switched wearable fitness devices. Using the Push-Pull-Mooring theoretical framework as a guide, the analysis revealed ten distinct themes influencing users\u27 decisions to switch devices. This research contributes to IS Replacement and provides practical insights for WFD manufacturers seeking to understand customer switching behavior
The Influence of Organizational Culture on AI adoption and Organizational Performance
No full textRecently, the adoption of artificial intelligence (AI) has become increasingly popular for organizations to leverage existing enterprise databases to gain and improve organizational performance. Despite these benefits, many organizations are experiencing numerous challenges to adopt AI and gain a competitive advantage. While prior research focuses on AI\u27s technological capabilities, this study examines the interplay between organizational culture, AI adoption, and organizational performance using PLS-SEM. The results show that organizational culture positively impacts AI adoption and organizational performance. This study contributes theoretical knowledge to IS research and provides practical insights for managers to take advantage of AI