Computer Science and Mathematics

Abstract: The Evil Twin attack, which involves creating rogue Wi-Fi access points that impersonate legitimate networks, remains one of the most persistent and adaptive threats in cybersecurity, despite more than two decades having passed since its first public demonstration in 2005. This paper aims to provide a comprehensive analysis of the evolution of this attack, perceived as an “invisible enemy” due to its low detectability and systematic underestimation in incident reports. The study addresses key questions: how the Evil Twin attack has evolved, how its methods and tools have changed, where it currently stands, and where it may be heading in the future. The paper compiles evidence from conference presentations, academic publications, government reports, industry analyses, and media coverage, as well as selected defense mechanisms such as WIPS, WPA3, Protected Management Frames, ETGuard, and the Trusted Wireless Environment framework. An original taxonomy of Evil Twin attack mutations is proposed, along with a ten-stage Kill Chain model ([A]–[J]) mapped onto the MITRE ATT&CK framework, an exposure time metric T_e as a key evolutionary parameter, and models quantifying attack cost-effectiveness and efficiency. The analysis demonstrates that the Evil Twin remains a persistent and adaptive threat, whose effectiveness stems from the combination of technical vulnerabilities, user trust in familiar network names, and the difficulty of unambiguous attribution and classification of incidents.

Abstract: Bit allocation is a core design problem in spatially correlated sensor fields under limited communication resources since per-sensor bit depth determines quantization fidelity and thus the quality of acquired information. We address this problem by formulating bit allocation as a cooperative game whose payoff is given in the criterion of mutual information, and by using Shapley value to quantify each sensor’s contribution; to ensure this formulation scales well in larger networks, we approximate Shapley values via Neyman stratified sampling. We compare Shapley value-based allocation against four heuristic baselines – uniform allocation, greedy allocation, Voronoi-based geometry-aware allocation, and conditional variance-based allocation – with both randomly distributed and clustered deployments, using five complementary metrics: mutual information, global RMSE, boundary RMSE, worst-10% RMSE, and weighted posterior trace. Numerical experiments on sampled random fields show that stratified sampling achieves tight efficiency consistency with reasonable runtime and scales to larger sensor counts. Reconstruction performance is context-dependent: geometry-aware allocation often performs best under tight budgets, particularly on boundary and tail errors, while Shapley value-based allocation yields the best performance in stringent small-scale fields and becomes competitive under high budgets for global and tail errors. Overall, mutual information and weighted posterior trace provide complementary rankings, highlighting trade-offs between information-centric objectives and reconstruction-error objectives under heterogeneous spatial redundancy.

Abstract: Transport Layer Security (TLS) protects most Internet traffic, yet migration to post-quantum TLS (PQ-TLS) depends on more than publishing a new algorithm standard. Despite rapid standardization of the Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM) and hybrid TLS groups, practitioners lack a structured way to assess whether deployment ecosystems are ready to adopt PQ-TLS. This letter proposes a six-dimensional readiness and adoption framework and a reproducible maturity rubric (levels 0--4). A case study of Windows, Linux, macOS, and Android applies the rubric to cited vendor documentation. The main finding is that cryptographic and protocol readiness are uniformly high across the case study, while platform and application readiness show the widest gap and act as the primary adoption bottlenecks for software that inherits OS TLS APIs. The study reports documented transition signals, not live PQ-TLS negotiation rates.

Abstract: Frontier AI systems with extended-context reasoning, scaffolded autonomy, native tool integration, and persistent operational state—defined operationally in prior work [1] as the Mythos-class category by five indicators (capability, scaffold, access pattern, autonomy depth, persistence)—exhibit a distinctive behavioral regime when deployed in adversarial settings: many individually sub-threshold actions compose into composite operations whose telemetry footprint is absent, fragmented, or below the per-event review threshold the deployment has instrumented. The behavior is discontinuous in the observer’s frame even when the AI system’s underlying action sequence is continuous, and observability-centered detection regimes are structurally calibrated to miss it. The prior reference architecture detects these behaviors empirically through supervisability-evasion signatures (output-fragmentation, latency-modulation, scope-creep) at the runtime tier; what remains unspecified is the theoretical scaffolding under which those signatures cohere as a single behavioral phenomenon. This manuscript develops four contributions. First, a relational systems-theoretic model characterizing Mythos-class behavior as a frame-shift across three coupled observation frames (identity, trust, telemetry), defined by three constitutive properties (non-locality, non-sequentiality, observability collapse). Second, a four-class taxonomy partitioning AI behavioral discontinuity at the relational level: presence, privilege, domain, and observability. Third, a cross-operation detection matrix specifying primary detection mechanisms for each class, deployable atop telemetry the prior architecture’s instrumented surfaces already produce. Fourth, integration extensions routing the new signals through the prior architecture’s mitigation stack without parallel architectural primitives. The framework is illustrated through a synthetic deployment scenario and grounded in systems-theoretic precedents (Ashby, Luhmann). The contribution characterizes a behavioral regime specific to frontier AI capability rather than competing with existing detection frameworks; classical artifact-centric models such as the Cyber Kill Chain and MITRE ATT&CK are treated as the continuous-traversal baseline against which the discontinuity layer is specified.

Abstract: TinyML autoencoder anomaly detection is widely proposed for embedded sensor networks because the autoencoder’s learned latent representation supports downstream signal characterization that pure threshold detectors structurally cannot. However, the standard frozen-threshold architecture relies on a calibration-time frozen reconstruction-error threshold whose validity has not been characterized on signals containing slow envelope drift. We test a Hammad-style multilayer-perceptron autoencoder baseline against the non-stationary noise model previously used to validate the Temporal Spectral Noise-Floor Adaptation (TSNFA) detector [1] on Cortex-M4F-class hardware, in both per-node-trained and shared-pre-trained variants. Across the configuration matrix we find a structural false-alarm-rate floor in the order of one thousand false alarms per hour per node, two to three orders of magnitude above TSNFA on the same input realisations. Sweeping the proportion of training frames containing transient bursts and the threshold coefficient confirms the ceiling is not transient-driven but correlated to drifting noise. We then introduce a hybrid architecture in which a single scalar drift estimate sourced from a TSNFA detector normalizes each frame before the autoencoder receives it, leaving the autoencoder weights and frozen threshold unchanged. The hybrid delivers two quantified findings. First, full suppression of false positives: the false-alarm cluster rate collapses from 17.95 clusters per hour per node (MLP TinyML-Shared baseline) to 0.00 clusters per hour per node (hybrid) at 12 dB SNR on a 50-node network, matching TSNFA on the same input realisations, with network load reduced by a factor of 260. Second, signal classification: the autoencoder's 8-dimensional bottleneck output separates background noise from two synthetic event classes at 96.0 % balanced accuracy in the hybrid, against 83.1 % in the baseline detector under the same drift — a feature the binary TSNFA detector cannot provide. The hybrid is therefore not a replacement for TSNFA on detection alone-TSNFA dominates at roughly 200× lower compute, but it becomes the operationally-preferred deployment path whenever downstream signal characterization is required alongside binary detection.

Abstract: In this paper, we examine how the VTP2 extended persistence timeout policy affects and influences the performance of distance-vector and link-state routing protocols in the ad hoc network of the New Generation of Open Digital Universities (DOUNG). The problem addressed is that conventional SCTP retransmissions lack good performance when losses result from a path break rather than congestion. In classical SCTP, missing acknowledgments may trigger retransmissions even when the loss is caused by a temporary route failure rather than by congestion. The proposed evaluation uses an NS-3-compatible methodology with IEEE 802.11, SCTP, AODV, DSDV, DSR and OLSR under increasing node mobility. Results are organized by protocol to improve figure readability. The reference outputs show that VTP2 improves packet delivery ratio, throughput, end-to-end delay, SCTP retransmissions and energy consumption. The average gains are higher for AODV, DSDV and DSR than for OLSR, confirming that extended persistence is more beneficial to protocols exposed to route discovery, repair and maintenance phases. These results indicate that VTP2 is a relevant cross-layer mechanism for improving quality of service in mobile, heterogeneous and distributed digital-university environments.

Abstract: Autonomous Underwater Vehicles (AUVs) have emerged as an effective solution for data collection in Underwater Wireless Sensor Networks (UWSNs), addressing fundamental limitations of acoustic communication such as limited bandwidth, long propagation delays, and high error rates. By moving close to each sensor node for direct data retrieval, AUVs improve energy balance, extend network lifetime, and enhance coverage flexibility. However, AUV-assisted data collection introduces complex challenges, including trajectory optimization under energy, latency, and coverage constraints, as well as robustness to dynamic ocean environments, intermittent connectivity, and large-scale multi-AUV coordination. This survey presents a systematic review of 56 representative AUV-assisted data gathering protocols (2011–2025) and introduces a unified six-class taxonomy that consolidates fragmented classifications in the literature. Following a structured screening of more than 200 peer-reviewed records, these 56 protocols were selected for detailed taxonomy-based analysis. The proposed taxonomy spans classical trajectory optimization, clustering-based organization, learning-based single-AUV methods, multi-AUV coordination, hybrid communication and quality-aware strategies, and energy and lifetime management. In addition, we provide a comparative analysis across key performance dimensions, including energy efficiency, network lifetime, latency, Age of Information (AoI), delivery reliability, and scalability, highlighting fundamental trade-offs among these metrics. Our analysis reveals a clear shift toward learning-based and AoI-driven approaches, while identifying critical gaps in real-world validation, scalability of multi-AUV systems, and security-aware cross-layer design. Finally, we outline open research challenges and future directions to guide the development of robust, scalable, and deployable AUV-assisted data collection systems for next-generation ocean monitoring applications.

Abstract: Wildlife-vehicle collisions (WVCs) cause approximately 570 human fatalities in Canada per 20-year cohort, with Alberta accounting for 22% of these and incurring an estimated CAD $300,000 per day in direct and indirect costs. Wildlife fencing combined with crossing structures reduces collisions by ~86% on well-instrumented sites but remains economically infeasible across the majority of rural road kilometres, leaving a substantial collision residual. We present a combined sensor network integrating alternating-side radar nodes (10-m spacing baseline), three-axis magnetometers, dynamic message signs, and LoRa-mediated awareness propagation between adjacent radars. System performance is evaluated through a discrete-time Monte Carlo simulation on a 1 km test corridor, incorporating a six-state animal behavioural Markov model with vehicle-threat-dependent decision branching, Intelligent Driver Model vehicle dynamics, and a three-mode contrast that isolates the contributions of sensing, driver alerting, and network coordination. Across 60 independent trials, the integrated system reduces the collision rate per road entry by 47.4% relative to an unmitigated control (Welch's t = 2.82, p < 0.01), and simultaneously increases safe road-crossing throughput by 77% by lowering the perceived vehicle threat that otherwise triggers pre-crossing retreats. Sensitivity sweeps establish a statistically significant equivalent-performance band across 5-20 m alternating radar spacing and across small-to-medium animal classes (fox- through deer-class), with operational robustness against tenfold degradation of baseline sensor sensitivity. A conservative 20 m alternating deployment spacing is recommended to provide engineering margin against range-dependent radar SNR, clutter, and environmental factors not captured in the idealized detection model. The architecture complements existing fence-and-crossing infrastructure at approximately one order of magnitude lower per-kilometre cost.

Abstract: Industrial systems increasingly rely on MQTT-based message streaming to enable automated, data-driven production processes at the network edge. While semantic models such as the SSN/SOSA ontology enable machine-interpretable descriptions of observations and actuations, an explicit model of message transport is rarely considered. Consequently, MQTT-based communication remains opaque, particularly regarding information processing, hindering the semantic analysis of application-specific topic structures and the behavior of transport protocols. To close this gap, this work introduces the revised MQTT4SSN ontology as a key contribution, extending existing semantic models with protocol-aware representations of MQTT entities, control packets, and transport-level interactions. MQTT4SSN enables end-to-end semantic traceability, from sensor observations and actuator controls to the underlying message transmission within distributed systems. Building on this contribution, the MQTT2RDF integration framework incorporates MQTT4SSN as its core to capture live MQTT traffic and represent both payload meaning and transport-level provenance within an RDF knowledge graph. This work presents a novel approach for representing edge computing and information processing over MQTT, addressing two key challenges. First, a semantic topic-naming approach automatically derives MQTT topic hierarchies and payload content structures from observation and actuation semantics. This approach facilitates the setup of edge computing systems and enables context-aware subscription management and structured data formatting, thereby improving interoperability between heterogeneous deployments. Second, transport-level provenance analytics support automated detection, classification, and root cause analysis of malformed MQTT packets and protocol-level errors. The approach provides explainable, traceable information processing through transport provenance, which is essential for safety-critical industrial environments. The contributions are validated through an industrial use case from a production environment, demonstrating its applicability for system monitoring, troubleshooting, and semantic analytics of MQTT-based infrastructures.

Abstract: Cloud computing and mobile edge computing address the growing demand for computing power driven by the rise in data-intensive applications, but they are prone to creating computing silos, resulting in unbalanced resource utilization. To address this issue, the Computing Power Network (CPN) has been introduced to enable the centralized management and scheduling of resources across the entire network. However, task scheduling in the CPN requires joint selection of computation nodes and routing paths, which greatly increases the complexity of scheduling problem. In existing studies, heuristic methods are difficult to satisfy real-time requirements, whereas deep reinforcement learning methods ignore the collaborative optimization of network resources, making them difficult to adapt to complex CPN scenarios. To this end, we propose a task scheduling method for the CPN, called TS-DQNF. First, the method uses the Deep Q-Network (DQN) to determine the computation node for computation task. Then, it introduces a dynamic congestion-aware mechanism to determine the shortest routing path. Finally, it gradually obtains the optimal task scheduling scheme through multiple rounds of alternating iterations. Simulation results show that the TS-DQNF achieves good performance and good convergence performance under different scenarios and scales.

Abstract: Unmannedaerial vehicle (UAV)-assisted millimeter-wave (mmWave) and terahertz (THz) communications are promising enablers of ultra-reliable and low-latency communication in next-generation wireless networks. However, the initial access and beam alignment process remains challenging because highly directional beams must be rapidly aligned in a three-dimensional environment. In this paper, we investigate a risk-aware beam alignment framework for UAV-assisted mmWave/THz systems, where user equipment scans a 3D spherical region to detect UAV base stations. The objective is to jointly minimize the expected cell-search latency and its variance while satisfying detection-failure and link-quality constraints. To solve this non-convex optimization problem efficiently, we employ the Lévy Self-Renewable Flow Direction Algorithm (LSRFDA), which combines Lévy-flight exploration with self-renewal to improve convergence robustness. A unified propagation model is adopted to cover both mmWave and THz regimes by incorporating free-space spreading loss and frequency-dependent molecular absorption. Extensive Monte Carlo simulations compare the proposed approach with Particle Swarm Optimization, Random Search, Reinforcement Learning, and PPO-Lagrangian methods. The results show that LSRFDA achieves lower latency, lower latency variation, more reliable detection, and lower energy consumption across a wide range of UAV densities and coverage radii. These outcomes highlight the effectiveness of risk-aware geometric optimization for fast and dependable initial access in UAV-assisted 5G mmWave and 6G THz networks.

Abstract: As Intelligent Transportation Systems (ITS) transition towards automated ecosystems, the deployment of advanced wireless charging technologies becomes a critical infrastructure requirement. Central to the management of these networks is the Open Charge Point Protocol (OCPP), which ensures interoperability across diverse hardware vendors. However, the reliance on digital communication for power transfer introduces significant cybersecurity vulnerabilities. This paper presents a methodology for evaluating the impact of cyber-threats on urban transport services, with a specific focus on the communication layers that support these Advanced Wireless Power Transfer (WPT) environments. Utilising Stochastic Petri net (SPN) ontology, we model the operational states of an Electric Vehicle (EV) service—including the activation and the arrival phases—to quantify how protocol-level vulnerabilities affect service reliability. We introduce an Extended Vulnerability List (EVL) and analyse two distinct scenarios: a public transport service and a weather forecasting integration. Our results demonstrate that as wireless charging moves towards standardization, the security of the OCPP-based backbone is a fundamental necessity for preventing service disruption. The proposed assessment framework provides a roadmap for securing the next generation of dynamic wireless charging infrastructures against evolving cyber-physical threats.

Abstract: Mobile edge computing (MEC) enables computation-intensive and latency-sensitive tasks to be offloaded from mobile devices to nearby edge servers. Most existing MEC task offloading studies formulate offloading as a selection problem over a fixed or fully available set of candidate servers, which is restrictive in heterogeneous MEC scenarios with task-node eligibility constraints. Under such constraints, a task can be processed by an edge server only when task attributes, service requirements, link conditions, and node states jointly satisfy the corresponding eligibility conditions. The feasible action set therefore varies over time, while offloading decisions are further coupled with local queueing competition and long-term load evolution. To address this problem, this paper proposes RoSCo, a load-aware task offloading method with scheduling and constraint coordination for eligibility-constrained MEC systems. RoSCo constructs a dynamic feasible action set, applies eligibility-aware action masking to exclude infeasible offloading actions, introduces priority-driven local coordination to characterize service competition among heterogeneous tasks, and designs a load-responsive reward to guide congestion mitigation and load balancing. The offloading policy is learned using a dueling double deep Q-network (D3QN). Simulation results show that RoSCo reduces task drop rate and edge-node load imbalance while maintaining competitive task completion delay and energy consumption, especially under high-load and sparse-eligibility conditions.

Abstract: This paper presents an explainable defense framework against perception-layer and Man-in-the-Middle (MitM) attacks in Internet of Things (IoT)-based environmental hazard warning systems. These systems rely on heterogeneous sensors (gas, light, sound, temperature, and humidity) whose integrity is crucial for reliable environmental alerts. Perception-layer attacks such as spoofing, jamming, and data injection can compromise sensor readings, while MitM attacks threaten communication reliability. The proposed approach integrates Dynamic Time Warping (DTW) for time-series anomaly detection with Shapley Additive Explanations (SHAP) for interpretability. A comparative evaluation framework jointly considers detection performance and explanation quality through metrics including pre-registering a Casual Ground Truth based on network protocol specifications and measuring the Sperman’s rank correlation of SHAP outputs, which eliminates the need for manual expert evaluation. Experimental simulations using an authentic EdgeIIoT-2022 dataset demonstrate high detection accuracy and moderated explainability scores. The results prove the framework’s ability to detect and explain adversarial behaviors in sensor networks, strengthening trust, transparency, and resilience in safety-critical IoT infrastructures.

Abstract: Self-regulated transportation networks belong to the class of continuous network models and are widely used not only in biological applications, such as vascular systems, neural networks or tissues regeneration but also in urban infrastructure and in communication technologies. Their well-established tree structure prevents the formation of loops, which limits their ability to capture an important feature observed in real systems: when a disruption or damage occurs, the network should be able to reorganize to restore transport pathways. In this work, we propose alternative modeling strategies to incorporate this capability. These approaches allow the network to adapt to perturbations by modifying its structure and, in some cases, by creating alternative routes that compensate for damaged regions. Numerical results illustrate how the modified models can reproduce self-repair mechanisms that are not captured by standard formulations.

Abstract: Post-Quantum Cryptography (PQC) migration to NIST FIPS 203, 204, and 205 under NSA CNSA 2.0 is a multi-year, multi-domain transformation across cloud, enterprise, embedded, OT, tactical, and national-security systems. Anthropic’s Claude Mythos Preview (April 2026) introduces AI-accelerated cybersecurity capabilities that intersect this migration directly, performing autonomous reasoning against previously unknown vulnerabilities in production software — a qualitative departure from signature-based and SAST/DAST tooling. Drawing on federal guidance from NIST, NSA, OMB, and CISA, and on independent analyses from CETaS and the UK AI Security Institute, we present a lifecycle and architecture analysis of how Mythos-class models alter PQC migration timelines, risk surfaces, lifecycle dependencies, and architectural constraints. Modeling Mythos as both accelerator and destabilizer, we derive an analytic projection of a compressed two-to-four-year migration window for highest-exposure systems, against traditional baselines of five-to-ten years for small organizations and twelve-to-fifteen-plus years for large enterprises. The compression collapses human-labor bottlenecks in discovery, planning, and code modification, not cryptography itself. We propose a lifecycle-aligned migration model, an updated cost model, and governance requirements for frontier-model access. The binding constraint shifts domain-conditionally: defender capacity at adversary tempo governs software-analytical phases, while non-compressible external cadence governs embedded and regulated domains.

Abstract: Fixed-allocation and loosely coordinated cooperative sensing frameworks are structurally inadequate for the spectrum management demands of 5G-Advanced and emerging 6G networks, as both treat sensing and allocation as decoupled processes unable to satisfy primary user protection, service-level agreements, and edge-native latency constraints simultaneously. This paper proposes an edge-native Spectrum-as-a-Service (SpaaS) framework based on the Interchangeable Spectrum Sensing Scheduling (ISSS) algorithm, in which sensing is treated as a schedulable, cost-bearing resource jointly optimized with spectrum allocation at the network edge. A formal system model is developed defining spectrum availability, sensing cost, service utility, and regulatory constraints as coupled elements of a single optimization structure, solved through a linear-complexity, single-pass heuristic enabling real-time execution. The framework is evaluated through Monte Carlo simulation under three primary user activity regimes against both a single-edge baseline and a cooperative sensing configuration at equivalent node count. Pareto efficiency frontier analysis identifies ten coordinated edge nodes as the optimal coordination density, at which point ISSS achieves an interference reduction gain of 87%, a spectrum utilization gain of 43%, and a scheduling efficiency gain of 27% over the single-edge baseline. These results establish ISSS as a practical, policy-aware, and scalable mechanism for dynamic spectrum orchestration in future wireless networks.

Abstract: Anthropic’s April 2026 release of Claude Mythos Preview, and the subsequent emergence of “Mythos-class” as a descriptor for frontier autonomous offensive cyber capability, has prompted institutional response across financial regulation, but no blockchain-specific analytical or policy framework. This paper develops one. We define Mythos-class as a vendor-neutral capability profile comprising five primitives — autonomous discovery at codebase scale, multi-step exploit chaining, agentic execution with tool use, sub-day weaponization, and generality across target classes — and we engage the contested boundary between maximalist and distributional framings of the capability through analysis of independent evaluations by AISI and AISLE. The central thesis the paper defends is friction inversion: the patch primitives, segmentation, vendor-coordinated disclosure, and credential rotation that constrain Mythos-class capability in conventional IT environments are not reduced on-chain but structurally absent, making blockchain systemic exposure differently positioned in kind, not in degree, from enterprise IT exposure. We instantiate the thesis against Bitcoin and Ethereum/L2 architectures and four bridge case studies (Ronin, Wormhole, Nomad, Poly Network) totaling over $1.74 billion in losses. Vendor-neutral defensive and governance frameworks defined against the capability profile rather than against any specific model release are the correct unit of analysis. Recommendations follow for protocol governance, audit cadence, and regulatory posture.

Abstract: Artificial intelligence (AI) systems increasingly depend on multi-stage supply chains that incorporate pre-trained models, third-party datasets, open-source libraries, and automated pipelines, creating an expanding attack surface in which model poisoning, dependency compromise, and provenance manipulation can undermine integrity before deployment. Existing AI governance frameworks—including the NIST AI Risk Management Framework and Secure Software Development Framework—acknowledge supply chain risks but do not define verifiable model provenance or cryptographically durable integrity guarantees. The transition to post-quantum cryptography (PQC) compounds this gap: classical digital signatures used to verify model lineage, dataset integrity, and pipeline attestation will become vulnerable to quantum-enabled forgery within the operational lifetime of many AI systems. This paper synthesizes evidence from policy, standards, and incident sources to characterize the AI supply chain threat landscape and the cryptographic dependencies that the PQC transition disrupts. It proposes three integrated design-science artifacts: a Model Bill of Materials with PQC-safe extensions (MBOM-PQC) defining a verifiable provenance schema; a unified signing and attestation pipeline integrating ML-DSA and hybrid signature modes; and a five-level Supply Chain Assurance Maturity Model (SCAMM) for repeatable organizational evaluation. These contributions provide a structured foundation for AI supply chain integrity in cloud-connected, mission-critical smart systems, ensuring verifiable lineage, authenticity, and trustworthiness through the PQC transition. Empirical validation is deferred to future work.

Abstract: The Hill cipher has historically lacked the confusion and diffusion properties required for modern cryptographic use. This paper presents the Multidimensional Hill Substitution-Permutation Network (MD-Hill-SPN), a 128-bit, 12-round block cipher combining three elements: (1) a hierarchical matrix diffusion layer operating at 4×4, 8×8, and 16×16 byte scales over GF(2⁸); (2) two AES S-box substitution layers per round; and (3) Argon2id memory-hard key derivation. Metric sessions used a SHA-256 domain-separator surrogate for Argon2id for computational tractability; Argon2id is the specified production KDF. Two independent runs of the full metric suite yield: (a) full plaintext avalanche from round 1 (mean 63.97–64.67 of 128 bits, ideal 64); (b) the differential-probability sampling floor of 2×10⁻⁵ reached at round 4 (50,000 of 50,000 output differences distinct, both sessions); (c) algebraic-degree lower-bound saturation at the maximum observable value from round 1; (d) linear bias indistinguishable from random (combined exceedance 4.40%, below the 4.55% noise floor); and (e) branch numbers at the Singleton (MDS) bound for every tier (B = 5 for 4×4, B = 9 for 8×8, B = 17 for 16×16), computed exhaustively over weight-1 inputs. MD-Hill-SPN therefore moves beyond theoretical construction to empirically validated confusion and diffusion properties stronger than prior Hill-cipher variants.