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Tran Duc Le

Publications and source records attributed to Tran Duc Le.

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Research Methodologies for Cybersecurity in Enterprise Environments: A Narrative Review, Synthesis and Executable Guide

Enterprise cybersecurity research draws on a wider range of methods than any single community routinely teaches. Researchers face a selection problem before they face a technical one: a study may simultaneously need a systematic review, a design-science artifact, a controlled detection experiment, an interview study, or an attack-graph model. This paper addresses that problem in two ways. First, it provides a narrative review and synthesis of methodological practices across a verified corpus of 151 works. We organise these practices into eleven methodology families, detailing for each what questions it answers, the strength of its supporting evidence, and its common failure modes. Second, we convert each family into an executable protocol comprising ordered steps, required instruments, evaluation criteria, common validity threats, and a reporting checklist. Every protocol is also visually mapped to make the sequence, decisions, and threats legible at a glance. We also treat contradictions in the literature as evidence. For example, reported rankings of intrusion-detection algorithms are wildly inconsistent across individually careful studies. We argue this pattern is most parsimoniously explained by variations in evaluation design rather than the algorithms themselves, as these studies differ in design dimensions known to shift results by more than the margins separating the algorithms. Ultimately, the evidence supports methodological pluralism disciplined by explicit validity reasoning. We conclude that researchers must match their evaluation design to the decision under study, triangulate technical against organisational evidence, explicitly state the population a result generalises to, and report the conditions under which the result would not hold.

cs.CR

Beyond the Flag: A Framework for Integrating Cybersecurity Competitions into K-12 Education for Cognitive Apprenticeship and Ethical Skill Development

Capture the Flag (CTF) competitions are powerful pedagogical tools for addressing the global cybersecurity workforce gap, yet their effective K-12 implementation is often undermined by significant barriers, including educator preparedness gaps and equity concerns. This paper addresses these challenges by proposing the Ethical-Cognitive Apprenticeship in Cybersecurity (ECAC) framework, a new model derived from a systematic Framework Synthesis of existing literature and empirical evidence. ECAC systematically integrates cognitive apprenticeship theory with embedded ethical development across five phases: (1) Foundational Modeling, (2) Scaffolding the Arena, (3) Coaching and Articulation, (4) Ethical Dilemma Injections, and (5) Reflective Exploration. The framework provides a "low floor, high ceiling" learning pathway designed to broaden participation among diverse student groups, including underrepresented minorities and women, while fostering deep, transferable skills. By reframing the educator role as a lead learner," ECAC also offers a sustainable solution to the teacher expertise gap. Ultimately, this framework provides a practical roadmap for transforming CTFs from standalone competitions into integral learning experiences that cultivate a more skilled, ethical, and diverse generation of cybersecurity professionals.

cs.CY

A Conceptual Model for AI Adoption in Financial Decision-Making: Addressing the Unique Challenges of Small and Medium-Sized Enterprises

The adoption of artificial intelligence (AI) offers transformative potential for small and medium-sized enterprises (SMEs), particularly in enhancing financial decision-making processes. However, SMEs often face significant barriers to implementing AI technologies, including limited resources, technical expertise, and data management capabilities. This paper presents a conceptual model for the adoption of AI in financial decision-making for SMEs. The proposed model addresses key challenges faced by SMEs, including limited resources, technical expertise, and data management capabilities. The model is structured into layers: data sources, data processing and integration, AI model deployment, decision support and automation, and validation and risk management. By implementing AI incrementally, SMEs can optimize financial forecasting, budgeting, investment strategies, and risk management. This paper highlights the importance of data quality and continuous model validation, providing a practical roadmap for SMEs to integrate AI into their financial operations. The study concludes with implications for SMEs adopting AI-driven financial processes and suggests areas for future research in AI applications for SME finance.

cs.AI

Are Enterprises Ready for Quantum-Safe Cybersecurity?

Quantum computing threatens to undermine classical cryptography by breaking widely deployed encryption and signature schemes. This paper examines enterprise readiness for quantum-safe cybersecurity through three perspectives: (i) the technologist view, assessing the maturity of post-quantum cryptography (PQC) and quantum key distribution (QKD); (ii) the enterprise (CISO/CIO) view, analyzing organizational awareness, risk management, and operational barriers; and (iii) the threat actor view, evaluating the evolving quantum threat and the urgency of migration. Using recent standards (e.g., NIST's 2024 PQC algorithms), industry surveys, and threat intelligence, we synthesize findings via a SWOT analysis to map strengths, weaknesses, opportunities, and threats. Results indicate uneven and generally insufficient preparedness: while PQC standards and niche QKD deployments signal technical progress, fewer than 5\% of enterprises have formal quantum-transition plans, and many underestimate "harvest now, decrypt later" risks. Financial, telecom, and government sectors have begun migration, but most industries remain exploratory or stalled by costs, complexity, and skills gaps. Expert consensus places cryptanalytically relevant quantum computers in the 2030s, yet delayed preparation could leave today's data vulnerable for decades. We recommend immediate steps: establishing crypto-agility, creating quantum transition roadmaps, prioritizing PQC deployment in high-value systems, and upskilling cybersecurity teams. A coordinated, proactive approach is essential to secure current and future digital assets in the quantum era.

cs.CR

Raising the Bar: An Asymptotic Comparison of Classical and Quantum Shortest Path Algorithms

The Single-Source Shortest Path (SSSP) problem is a cornerstone of computer science with vast applications, for which Dijkstra's algorithm has long been the classical baseline. While various quantum algorithms have been proposed, their performance has typically been benchmarked against this decades-old approach. This landscape was recently reshaped by the introduction of a new classical algorithm by Duan et al. with a complexity of $O(m \cdot (\log n)^{2/3})$. This development necessitates a re-evaluation of the quantum advantage narrative for SSSP. In this paper, we conduct a systematic theoretical comparison of modern quantum and classical SSSP algorithms in light of this new classical frontier. Through an analysis of their theoretical cost functions, we illustrate how their relative scaling compares across scenarios that vary in graph density and path length. Our analysis suggests a nuanced picture: sophisticated quantum algorithms, such as the one by Wesolowski and Piddock, can exhibit more favorable asymptotic scaling, but only in regimes characterized by short solution paths. Conversely, for problems involving long paths, state-of-the-art classical algorithms appear to maintain a scaling advantage. Our work provides an updated perspective for future quantum algorithm development and underscores that the pursuit of quantum advantage is a dynamic race where the classical goalposts are continually shifting.

quant-ph

Challenges in Applying Variational Quantum Algorithms to Dynamic Satellite Network Routing

Applying near-term variational quantum algorithms to the problem of dynamic satellite network routing represents a promising direction for quantum computing. In this work, we provide a critical evaluation of two major approaches: static quantum optimizers such as the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA) for offline route computation, and Quantum Reinforcement Learning (QRL) methods for online decision-making. Using ideal, noise-free simulations, we find that these algorithms face significant challenges. Specifically, static optimizers are unable to solve even a classically easy 4-node shortest path problem due to the complexity of the optimization landscape. Likewise, a basic QRL agent based on policy gradient methods fails to learn a useful routing strategy in a dynamic 8-node environment and performs no better than random actions. These negative findings highlight key obstacles that must be addressed before quantum algorithms can offer real advantages in communication networks. We discuss the underlying causes of these limitations, including barren plateaus and learning instability, and suggest future research directions to overcome them.

quant-ph

A Genetic Algorithm Framework for Optimizing Three-Impulse Orbital Transfers with Poliastro Simulation

Orbital maneuver planning is a critical aspect of mission design, aimed at minimizing propellant consumption, which is directly correlated with the total velocity change ($ΔV$). While analytical solutions like the Hohmann and Bi-elliptic transfers offer optimal strategies for specific cases, they lack the flexibility for more general optimization problems. This paper presents a computational framework that couples a Genetic Algorithm (GA) with the Poliastro orbital mechanics library to autonomously discover fuel-optimal, three-impulse transfer trajectories between coplanar circular orbits. We validate this framework across two distinct scenarios: a low-energy transfer from Low Earth Orbit (LEO) to a Geostationary Orbit (GEO), and a high-energy transfer to a distant orbit with a radius 20 times that of LEO. Our results demonstrate the framework's remarkable adaptability. For the LEO-to-GEO transfer, the GA precisely converges to the classical Hohmann transfer, achieving an identical $ΔV$ of 3853.96 m/s and validating the method's accuracy. Conversely, for the high-energy transfer, the GA identifies a superior Bi-elliptic trajectory that yields a significant $ΔV$ saving of 213.47 m/s compared to the Hohmann transfer. This fuel efficiency, however, necessitates a trade-off, extending the mission duration from approximately 1 day to over 140 years. This work demonstrates an accessible and powerful toolchain for the rapid prototyping of optimal trajectories, showcasing how combining evolutionary algorithms with open-source libraries provides a robust method for solving complex astrodynamics problems and quantifying their critical design trade-offs.

astro-ph.EP