Computer Science and Mathematics

Abstract: The Internet of Things (IoT) has become embedded in everyday consumer environments through smart home devices, wearables, connected appliances, and related technologies. These devices offer substantial convenience and functionality while creating persistent privacy and security concerns. This exploratory study examines how consumer IoT familiarity, usage, privacy and security concerns, awareness of data practices, and self-reported protective behaviors relate to one another. The study is interpreted through Protection Motivation Theory (PMT), the Technology Acceptance Model (TAM), and Risk Perception Theory (RPT). A cross-sectional survey was distributed to 423 members of the University at Albany community and yielded 30 complete responses (7.1%). Quantitative analyses used descriptive statistics, correlations, and a t-test, while open-ended responses were summarized to provide qualitative context. Respondents reported high familiarity and frequent IoT use alongside substantial privacy and security concern, but many demonstrated limited knowledge of the data collected by their devices and inconsistent protective practices. The hypothesis tests were not statistically significant at the .05 level: privacy concern and IoT usage were weakly negatively correlated (r = -0.167, p = .377); perceived risk and awareness were weakly positively correlated (r = .121, p = .525); the awareness-group comparison was nonsignificant (t = .717, p = .479); and trust in vendor privacy protection showed a moderate positive association with IoT usage that did not reach conventional significance (r = .334, p = .071). The results therefore provide exploratory rather than confirmatory evidence of an adoption concern paradox: participants often continued to use IoT technologies despite privacy and security concerns. The findings are consistent with the possibility that perceived usefulness, convenience, risk appraisal, self-efficacy, and trust jointly shape consumer behavior, but the small nonprobability sample and cross-sectional design preclude causal inference and broad generalization. Importantly, this study measures perceived risk and awareness; it does not independently conduct technical vulnerability assessments of participants' devices. This study contributes by integrating PMT, TAM, and RPT to interpret the coexistence of IoT adoption and privacy/security concern; documenting mismatches among concern, awareness, and self-reported protective behavior in an exploratory university sample; and identifying implications for user education, secure-by-default product design, vendor transparency, and future research that combines behavioral measures with objective device-security assessments.

Abstract: Spoofing attacks pose a critical threat to Unmanned Aerial Vehicle (UAV) tracking, inducing trajectory drift, false-target injection, and corrupted multi-sensor measurements that destabilize classical association pipelines such as JPDA and GNN. This paper introduces the Quantum Unified Trajectory Estimator (CUTE-SpoofTrack), a quantum-inspired framework and benchmark for resilient multi-sensor UAV tracking under adversarial spoofing. The system integrates SpoofNet, a deep spoof-detection module that identifies temporal and spatial spoof signatures, with confidence-aware fusion and a Deep Quantum Neural Network (DQNN) that performs assignment reasoning through qubit superposition, Hadamard branching, and CNOT-based dependency encoding. This architecture maintains multiple association hypotheses and suppresses spoof-dominated states, providing robustness beyond classical argmax-based trackers. CUTE-SpoofTrack includes a reproducible benchmark with diverse spoof types—drift, ghost, mirror, burst, and multi-ghost—and provides interpretability overlays for analyzing assignment dynamics and quantum gate activations. Theoretical analysis establishes continuity, margin-based robustness, KL-stability, and spoof-aware amplitude suppression for the DQNN assignment distribution. Numerical evaluations demonstrate that CUTE significantly improves spoof detection accuracy, fusion stability, assignment consistency, and trajectory continuity compared to CNN,LSTM,GNN,JPDA,andfusion-agnostic baselines, while remaining within real-time operational constraints. The results position CUTE-SpoofTrack as a principled and practical foundation for resilient UAV tracking in adversarial aerial environments.

Abstract: Cross-silo deepfake speech detection must accommodate organizations that observe different attack profiles and channel conditions while avoiding routine centralization of raw speech. We study federated learning (FL) for a compact detector combining linear-frequency cepstral coefficients (LFCCs) with frozen WavLM representations over heterogeneous partitions derived from ASVspoof 2019 and 2021. An official-metadata audit of the historical seed-42 Client-C split reproduces the reported 2,572/3,655 (70.37%) bona-fide source overlap; a stricter unified-source audit against the complete A+B+C fit pool finds still greater dependence. We therefore repeat the principal audio evaluation with five source-disjoint splits whose holdouts are independently verified against the complete fit pool. Mean equal error rate (EER) is 3.836% for pooled centralized training, 4.622% for Federated Averaging (FedAvg), 4.095% for local C, 21.883% for local A, and 33.398% for local B. On the common Client-C holdout, A-only and B-only models transfer poorly, while FedAvg is on average 0.787 percentage points worse than pooled training and 0.527 points worse than local C; these comparisons do not establish native-domain benefit for A or B. Separately, a 20-seed controlled stress test examines integrity mechanisms under malicious participation. Increasing untargeted label-flip compromise produces progressively more negative benign--malicious update alignment and stronger aggregate-step attenuation; the signature persists across four classifier topologies and FedProx settings. Additional tests cover targeted label poisoning, a controlled trigger backdoor, zero-update free riding, and coordinated sign-flip model poisoning. We frame the contribution as an auditable experimental protocol and a bounded set of observations, not as a new detector, optimizer, attack, aggregator, or universal defense.

Abstract: We present the Defactorized Homomorphic Polynomial Public Key (D-HPPK) Encapsulation Mechanism, a post-quantum KEM whose security rests on exhaustive-search problems with efficient verification for both attack paths. We adopt the NIST search-based convention, under which a primitive is assigned security level l when the adversary's best-known attack requires exhaustive search over 2^l candidates with efficient verification, exactly as AES-128, AES-192, and AES-256 are assigned NIST Levels I, III, and V. For D-HPPK, both attack paths—long-term key recovery and ephemeral-secret recovery—are exhaustive searches over spaces of size 2^l with polynomial-time verification. The long-term key-recovery path reduces to the Hidden Modulus Product Problem (HMPP), and the ephemeral-secret recovery path reduces to the scheme-induced Message Recovery Problem (MRP). The scheme uses a three-layer architecture of additive random masking and hidden-ring embeddings. The hidden-ring moduli and multipliers block the public-constraint attack; the random masking destroys the rank-2 algebraic regularity of the unmasked coefficient matrices, forcing the adversary into HMPP. Both attack paths have best-known classical cost O(2^l⋅poly⁡(L)). The IND-CCA2 proof uses a double-encryption consistency check with a five-class rejection set, together with a small number of explicitly stated hardness assumptions; the adaptive form of the decryption-collision property is derived through Structured D-HMPP. A unified parameter set (n=2,m=3,l=⌈log₂⁡p⌉) supports a single implementation across security levels. At Level V (l=256), D-HPPK achieves a 1234-byte public key and a 392-byte CCA2 ciphertext—21% smaller public keys and 75\% smaller ciphertexts than ML-KEM-1024—without SIMD or NTT acceleration.

Abstract: The Internet's critical infrastructure relies on centralized trust authorities, and their repeated failures expose a structural fragility: these systems lack self-verifiability — the property that any party relying on a claim can independently verify that event or state claim without depending on any third-party authority. Here we propose the Memory-Driven Trust Paradigm, which endows digital infrastructure with self-verifiability through verifiable memory, shifting the fundamental question of trust from "whom to trust" to "what to verify." We define verifiable memory as cryptographically committed, independently verifiable traces of discrete events and states, and formalize it as an "Accumulate–Anchor–Verify" triadic structure: a unified framework neutral to the underlying commitment mechanism, revealing that previously fragmented self-verifiable schemes — blockchains, certificate-transparency logs, and verifiable storage — share the same deep logic. We characterize necessary and sufficient conditions for verifiable memory and give a constructive proof: an epoch hash chain with geometric staged scheduling reduces the worst-case membership witness from Θ(√n) to Θ(n¹ᐟ³) even under unbounded append; we further establish a mechanism-dependence boundary across verification dimensions, closure of constructions under composition, and a verification-baseline reset via anchoring. We instantiate the framework as four kinds of memory — blockchains for transaction memory, message chains for transmission memory, key chains for protection memory, and verifiable AI memory for inter-agent trust — of which the blockchain and its supporting systems have been validated in real-network deployment, and the message chain and key chain have undergone prototype evaluation and comparative experiments. This work establishes self-verifiability as a foundational property of digital infrastructure and provides a verifiable trust foundation for the AI-mediated digital era.

Abstract: Firewalls are audited, reverse-engineered and tested by sending packets and observing whether they pass, yet the literature offers algorithms, not lower bounds. We formulate black-box firewall verification as a query-complexity problem in Angluin's exact-learning framework, with oracles matched to what auditors and attackers observe: action-only queries, identity queries (hit counters, and flow logs where they are exact) and witness packets from captured traffic. We prove that shadowed rules are invisible to every black-box observer; that without side information even a single-rule policy on the IPv4 5-tuple needs 2¹⁰⁴ − 1 probes; and that with one witness per rule and identity feedback the reconstruction cost is governed by the number of leaves m of the policy's minimal Tree-Rule — (m − 1)(log W + d) + 2m probes, in which the number d of header fields is only an additive term, against 2n·Σ log W_j + n for a disjoint list of n rules, where d multiplies the logarithm. A fixed certificate of 3m − 2 packets is complete against structural misconfigurations and Θ(log W) cheaper than reconstruction, and N traffic samples bound the expected reconstruction error by m/(eN). Exact simulations, a real iptables testbed and 16 production rule sets confirm the bounds and expose a dichotomy: policies with small canonical trees are 13–26× cheaper to audit as trees, whereas cross-product policies inflate the tree by up to 83×, making the query complexity of overlapping rule lists the central open problem.

Abstract: Agentic cyber defense compresses the interval between detection and consequential action, but existing practice primarily treats speed as a performance objective or response cost. This article argues that autonomous defenders should govern not only what action to take but also when, how quickly, how broadly, how reversibly, and under whose authority it should proceed. It introduces Temporally Reflexive Cyber Defense, a six-layer architecture that converts temporal epistemic agency and dromological analysis into operational controls for incident response. Eighteen design primitives address demonstrated urgency, bounded waiting, prospective reversibility, staged propagation, temporal memory, human capability, mission authority, accident discovery, and effective contestability. A worked credential-compromise example shows how hard authority rules and a lexicographic selector can produce decisions that differ from a nominal loss-minimizing baseline. The evaluation agenda uses matched cost-sensitive intrusion-response and autonomous-defense baselines to test average-loss noninferiority, severe-harm reduction under model error and adversarial telemetry, timing regret, incident reconstruction, analyst capability retention, capability-specific accident discovery, and contestability without unacceptable security loss. The proposal does not prescribe uniform delay. It makes pace an auditable control variable so that rapid reversible action remains possible while consequential automation preserves evidence, rollback capacity, mission continuity, and meaningful human intervention.

Abstract: Security authorization relies on a target security profile that is relatively stable, whereas threats, exposure, control health, and assessment evidence change continuously. We present a two-stage decision-support model that separates operational remediation from structural insufficiency of an already authorized profile. The first stage tests whether an assurance-adjusted authorization-risk index can be restored to tolerance using admissible actions while the profile remains fixed. Only when this problem is infeasible does the second stage generate a minimum-burden candidate profile for assessment and reauthorization. The model defines an authorization envelope, preserves mandatory requirements, distinguishes candidate from authorized states, and treats any structural profile revision as a reauthorization event. In a 365-day synthetic critical-infrastructure benchmark with 100 paired Monte Carlo replicates, the proposed strategy averaged 0.27 days above the benchmark risk tolerance and 1.43 risk-driven profile revisions, compared with 0 days and 9.06 revisions for direct event-driven reoptimization. Ablation and sensitivity analyses support the role of the remediation-first boundary while identifying assumed control effectiveness as the main source of model uncertainty. The results support frequent reassessment but selective, explainable profile revision. This study is a computational proof of concept and does not replace legal authorization or empirical cyber-range validation.

Abstract: The growing integration between cloud-based CRM platforms and AI-driven analytics services expands both operational benefits and the organizational attack surface, yet most published threat-modeling studies still validate STRIDE and LINDDUN in isolation, on synthetic or single-domain systems, without tracing findings through to standardized controls. This paper closes that gap by proposing an end-to-end, empirically validated methodological chain that combines Data Flow Diagrams, dual STRIDE/LINDDUN classification, OWASP Top 10:2025 prioritization, expert-panel refinement, and NIST SP 800-53 Rev. 5 control mapping, applied to a real production architecture in a large regulated organization integrating cloud CRM with AI analytics. Unlike prior work that applies a single framework or a generic reference architecture, this case study preserves full traceability from 28 mapped data flows to specific control codes, validated against nine security experts using a Delphi-inspired panel. The results reveal that threats concentrate disproportionately in federated authentication token channels and metadata catalogs, components that individually appear low-risk but structurally connect the ecosystem's highest-impact OWASP categories. This concentration pattern, not previously reported for CRM-AI integration architectures, indicates that authentication chains function as systemic single points of failure even when individual controls appear adequate. We argue this traceable, standards-aligned chain offers a replicable template for governance teams needing to justify control investments in similarly coupled cloud ecosystems, particularly under LGPD/GDPR compliance pressure.

Abstract: This study examines whether structured cybersecurity training meaningfully reduces behavioural risk in higher education institutions, addressing persistent gaps between awareness and secure practice identified in prior socio-technical research. Using an explanatory sequential mixed-methods design, the research integrates deep learning-based predictive modelling of behavioural risk scores (n = 250) with qualitative thematic analysis of institutional interviews. Survey constructs captured training exposure, policy knowledge, best-practice adherence, risk tolerance, technical readiness, and perceived cybersecurity culture. A multi-layer neural network model was developed and validated through cross-validation procedures to estimate behavioural risk, followed by regression, mediation, and moderation analyses. Results indicate that participants receiving structured interactive training exhibited substantially lower predicted behavioural risk than untrained or awareness-only groups, with training intensity demonstrating a significant dose–response effect. Hierarchical regression shows that training predicts behavioural risk beyond policy knowledge and cognitive factors, while mediation analysis confirms partial knowledge-based pathways. Moderation analysis reveals that cybersecurity culture amplifies training effectiveness. Qualitative findings corroborate statistical results, highlighting structural training deficiencies and cultural marginalisation as key governance weaknesses. The study concludes that structured, culturally embedded training functions as a central behavioural determinant, and that predictive analytics can support evidence-based cybersecurity governance in higher education.

Abstract: Deep learning models for Network Intrusion Detection Systems (NIDS) can detect attacks with high accuracy, but they work as black boxes. Security analysts cannot see why a model flags a certain flow as malicious. This limits trust and makes it harder to deploy these systems in practice. We propose HiT-IDS (Hierarchical Transformer for Intrusion Detection with Intrinsic Explainability), an architecture that detects attacks while also explaining its own decisions. HiT-IDS groups the 72 network flow features into four categories—temporal, volumetric, protocol, and behavioral—and uses separate intra-group Transformer encoders for each group, with per-feature tokenization. An inter-group Transformer then combines the group-level outputs. A two-stage cascade first separates benign from attack traffic (binary F1 > 0.98), then classifies attacks into 12 types. We test HiT-IDS on CIC-IDS2017 with 5-fold stratified cross-validation. Adaptive SMOTE balancing is applied inside each fold to avoid data leakage, with class-specific oversampling targets that prevent noisy synthetic generation for extreme minority classes. A two-phase training pipeline first optimises classification performance (Phase 1), then refines attention patterns through entropy regularisation (Phase 2). HiT-IDS reaches a macro-F1 of 0.9891 ± 0.0011 across 13 classes. We compare it against Random Forest (0.9989), XGBoost (0.9990), BiLSTM (0.9966), and Vanilla Transformer (0.9941). Paired t-tests, Cohen’s d, and 95% confidence intervals confirm that all differences are statistically significant. A primary strength of HiT-IDS is its built-in explainability. Hierarchical attention rollout shows that protocol-level features (importance: 0.306) matter most for attack detection. We employ a Spearman-based consistency check between intrinsic and post-hoc explanations, denoted XCS, to verify that attention-based feature importance agrees with SHAP values. We supplement attention rollout with input-gradient sensitivity (attention×gradient) to capture both information routing and feature sensitivity. XCS yields a mean Spearman ρ of 0.498 ± 0.103 (p < 0.05 for 99.2% of 500 samples, median ρ = 0.503). A cross-model XAI comparison confirms that HiT-IDS learns the same discriminative features as all baselines (per-sample SHAP agreement ρ = 0.65–0.75). Ablation studies confirm that each architectural component—hierarchical grouping, cascade detection, Phase 2 attention regularisation, and GELU activation—contributes to the final performance. HiT-IDS provides per-sample explanations at 0.55 ms/sample through attention rollout—approximately 870× faster than the average post-hoc SHAP cost in our setup—making it one of the few models in our comparison that combines real-time inference with per-sample hierarchical explanations.

Abstract: Large Language Models (LLMs) suffer from the problem of prompt injection vulnerability, but typical one-step defenses generally fail at the task by having a high amount of false positives. We introduce an innovative two-sided defense architecture that applies semantic multi-vector diversification and ensemble modeling to provide LLM protection without the need for closed black-box solutions. In our framework, we use three different semantic spaces mapping via publicly available embedding algorithms, where each is then scored by parallel classifiers based on gradient boosting machines. The inputs go through an adaptive voting-based routing process; those that receive low entropy ratings are classified normally, while high entropy items are sent through a secondary classifier – a local LLM expert, the Llama Guard 3, that handles the input. Importantly, this solution uses a two-way protection method, which checks the generated outputs and catches late execution and context manipulation vectors. Our experiments on a 93K dataset show that the ensemble approach performed better than any individual sub-model, reaching 90.69 % accuracy and 94.11 % specificity, minimizing any user experience disturbances. The multi-vector layer demonstrated high empirical resilience to tokenization errors, while the output layer filtered out many bypassed malicious items.

Abstract: Security Operations Centers (SOCs) face increasing pressure from alert fatigue and complex incident response requirements across heterogeneous production environments. While Large Language Models (LLMs) show promise in automating security workflows, their deployment in production settings raises significant concerns about safety, auditability, and controlled execution. We present SecOps-Agent, a policy-grounded framework that leverages LLMs for security incident response while maintaining strict operational controls. Our approach introduces a Policy Grounding Engine that validates all LLM-generated response plans against role-based access control (RBAC) policies, predefined playbooks mapped to MITRE ATT&CK techniques, and change management windows before execution. The framework enforces a whitelist-only action model with mandatory dry-run validation and generates comprehensive audit trails linking all actions to supporting evidence. Experimental evaluation on a composite dataset of WAF alerts, Kubernetes audit logs, and cloud security events demonstrates that SecOps-Agent reduces Mean Time to Respond (MTTR) by 47.3% compared to traditional SOAR automation while maintaining a false action rate below 5%. Our results indicate that policy-grounded LLM agents can provide meaningful automation benefits in security operations while preserving the auditability and control requirements essential for production environments.

Abstract: Existing hierarchical graph neural networks (GNNs) for blockchain fraud detection often suffer from scalability bottlenecks when processing large-scale transaction graphs. This paper proposes DLG-GNN, a Decoupled Local-to Global Graph Neural Network that separates contract-level local encoding from inter-contract relational reasoning. By combining domain-aware partitioning with a sequential inductive pipeline, DLG-GNN maintains peak GPU memory usage below 200 MB and main memory usage below 6.5 GB. Using GATv2-based local and global encoders, DLG-GNN demonstrates superior performance over representative baselines on Ethereum, BSC, and Polygon.

Abstract: The Internet of Things (IoT) has enabled smart electricity metering as part of smart city infrastructure, enabling automated consumption reporting, accurate billing, real-time outage visibility & demand response, and remote operational control. However; this advancement creates additional cyber physical attack surface in addition to the existing one, including those related to smart meters, gateways, brokers, edge nodes, and utility head end systems (HES). Recently reported literature continued to reflect growing concerns over IoT cybersecurity, smart meter threats, edge computing, and covert channel behavior in IoT environments. This article introduces Adaptive Steganography Trust Signaling (ASTS), which is a defensive network steganography framework that embeds very small, authenticated trust signals within normal smart meter traffic to enable tamper evidence, and edge level integrity verification. The purpose of malicious covert channels is to hide unauthorized communications; however, ASTS is both policy-bound and rate-limited for auditing purposes and to serve as an additional layer of support for conventional cryptographic authentication methods. The flow of smart electricity meter data (and other utility services) is supported by ASTS using bounded timing and ordering, which is provided by policy and protocol definitions within that framework. A risk-based decision model embedded in the framework identifies the appropriate method to embed ASTS into networks based on various factors including anomaly detection, network conditions, device trust levels, and the level of importance of the services being utilized. This paper provides algorithms for adaptive embedding, verification, and policy driven adaptation. A synthetic evaluation model representing periodic meter readings, replay attempts, tampering events, intermittent network degradation, and data manipulation events are used to evaluate performance prior to field deployments. The contribution of this work is a complete framework for adaptive defensive network steganography in smart electricity metering. From the experiments conducted, the proposed ASTS model outperformed static models, and related models from the state-of-the-art previous studies.

Abstract: Small office and home office networks are increasingly targeted by adversaries exploiting the growing population of IoT devices, yet most machine learning-based intrusion detection systems for IoT are limited to “attack classification” and provide little insight into where an intrusion sits within the broader attack lifecycle. This study develops a two-phase machine learning-based intrusion detection for small office and home office IoT traffic that extends conventional intrusion detection into “Cyber Kill Chain” stage classification and incorporates explainable AI to support transparent, actionable defences. Using the CICIoT2023 six-class dataset, Random Forest, Decision Tree and LightGBM were trained and evaluated. Random Forest achieved the highest accuracy while LightGBM offered a favourable trade-off between accuracy and computational efficiency. LightGBM was used to classify detected intrusions into four Cyber Kill Chain stages, achieving near-perfect precision and recall for reconnaissance and delivery-stage traffic, but comparatively weaker performance for exploitation and command-and-control traffic. Feature importance and SHAP analysis identified inter-arrival time and flow duration as the dominant drivers of stage classification. These findings show that machine learning-based intrusion detection can combine high accuracy with stage-aware explainable contextualisation, offering a practical foundation for proactive defence strategies in small office and home office environments.

Abstract: Healthcare has led every industry in average breach cost for more than a decade, and the largest recent incidents in the sector began with credentials that were valid at entry and were never re-verified afterwards. Continuous authentication targets that failure, but the literatures that could supply it are each incomplete: reinforcement-learning risk engines adapt without justifying their decisions, explainable-AI authentication explains only after the fact, and neither prices the cost of obstructing a clinician, which in a clinical setting is a patient-safety event rather than a usability nuisance. This paper presents the Hybrid Explainable Adaptive Authentication Framework (HEAAF), a zero-trust policy engine that produces an exact attribution inside the decision cycle and carries patient safety as an explicit policy term. HEAAF is evaluated by simulation: the behavioural channel is the real CMU keystroke-dynamics benchmark under a genuine template-ageing split, coupled to a documented clinical context generator supplying six labelled attack scenarios with ground-truth driver sets and hard negatives, with every policy read at a matched 1% clinical friction budget over eight seeds. Three results follow. Grouping makes exactness affordable: sixteen evidence features partition into eight semantic groups, so exact group Shapley values cost 2⁸ = 256 coalition evaluations in one batched pass, and a complete decision with an exact justification takes 0.611ms against 1.214ms for the same decision explained by approximate integrated gradients; we further show that summing feature attributions into groups is not the group Shapley value and that the two disagree on the dominant factor once in eight decisions. An explicit clinical safety valve removes emergency denials outright, from 1.10% to 0.000% for a G mean cost of 0.005, and the graded escalation ladder delivers the same detection for 7.6 rather than 26.5 clinician-hours of annual friction. Contrary to our starting hypothesis the reinforcement learning risk score is not the strongest detector: a supervised detector in the same policy engine gains 0.378 G-mean, which relocates the contribution of reinforcement learning to the graded action policy. Code, seeds and the scripts that regenerate every reported number are released.

Abstract: Intrusion detection systems often operate with incomplete information. Rule bases may contain gaps, attack types may be rare or previously unseen, labels may arrive late, sensors may capture only part of an event, and some observations may be poorly represented in the training data. However, many studies still use static closed-world evaluations and assume that providing an explanation makes a system trustworthy. This structured narrative survey examines explainable intrusion detection using the concept of incomplete information, defined as a lack of evidence needed to justify a label, score, abstention, escalation, or response. It distinguishes incomplete information from uncertainty, class imbalance, concept drift, privacy, federated learning, and adaptation. The review covers fuzzy rule interpolation, network and device evidence, post hoc explainable artificial intelligence, adversarial robustness and recovery, distributed and adaptive learning, and edge/fog/cloud deployment. Three main findings emerge. First, no single method addresses every information gap. Fuzzy rule interpola-tion directly addresses missing coverage in sparse fuzzy rule bases, while other methods are relevant only when they target a clearly defined information problem and are tested under matching conditions. Second, an explanation is credible only when its fidelity, stability, semantic validity, computational cost, and disclosure of information limits are evaluated for the intended task. Third, deployment design and governance determine whether an output can support operational action. The survey proposes a six-axis taxonomy, seven evidence streams, a tiered deployment architecture, an integrated evaluation and benchmarking protocol, design and reporting templates, and a research agenda. The framework links each conclusion to the tested information condition, stress model, computational tier, and authority boundary.

Abstract:

The growing integration of cyber, physical, and operational security functions has created a need for assessment approaches that evaluate security convergence as an organisational capability rather than as a set of isolated controls. This article proposes the Converged Security Maturity Index (CSMI), a conceptual maturity model designed to assess the level of integration among cybersecurity, physical security, and operational security within organisations. The model is developed using a design science research approach grounded in a structured review of converged security literature, cyber-physical system security, and existing maturity frameworks. CSMI comprises five progressive maturity levels—Fragmented, Coordinated, Integrated, Unified, and Optimised—and evaluates organisational capabilities across four dimensions: governance and strategy, processes and incident management, technology and infrastructure integration, and human factors and security culture. The article describes the architecture of the model, the rationale for each maturity level and dimension, and outlines how the framework could be applied in practice through a structured questionnaire in future work. The proposed framework aims to support organisational self-assessment, benchmarking, and identification of convergence gaps across different sectors.

Abstract: Established symmetric encryption schemes rely on short key lengths, typically between 128 and 256 bits. However, well-known physical key exchange schemes such as QKD (Quantum Key Distribution) and RKD (Radio Signal Key Distribution) generate megabits of new key material on a daily, weekly, or other basis. This article presents a new encryption scheme, called OTPH, which was specifically designed for these megabits of regularly generated new key material. In this scheme, the key material is treated as an extremely long key, and the key’s enormous entropy is fully exploited. OTPH first uses a bit-selection procedure that randomly selects bits from the circularly arranged key, thereby extending the key many times over. From this, a HKDF (Hash Key Derivation Function) derives many new keys, and the result is a bit sequence that serves as the key for encryption using an XOR operation. OTPH uses only the XOR operation and hash function, which are also used in QKD and RKD during postprocessing (albeit a different hash function). An initial cryptanalysis has also been performed for OTPH. OTPH operates in a manner entirely contrary to established methods. It requires a completely new perspective. OTPH will not compete with AES, ChaCha20 etc., but will instead be used primarily in the context of QKD and RKD.