Secure and efficient transmission of medical images requires integrating compression, encryption, andsubstitution mechanisms to protect sensitive patient data. Compression reduces image size, improving transmission andstorage while preserving diagnostic quality. Encryption ensures confidentiality and the Health Insurance Portability andAccountability Act (HIPAA) compliance, safeguarding patient information from unauthorized access. Nonlinearsubstitution using dynamic S-boxes increases confusion and strengthens resistance to cryptanalysis. This paper presentsa hybrid medical image encryption scheme comprising three sequential stages for high security and computationalefficiency. In preprocessing, images are resized, normalized, partitioned into uniform blocks, and compressed usingcompressive sensing based on partial discrete Fourier transform sampling. The encryption stage applies adaptive Sboxes with block interleaving, followed by the Advanced Encryption Standard in Galois/Counter Mode to provideintegrity-preserving encryption. During decryption, the Fast Iterative Shrinkage-Threshold Algorithm reconstructsimages accurately. Experimental results demonstrate that the scheme preserves image quality, achieves effectivecompression, and resists statistical and cryptographic attacks, making it suitable for secure medical imagecommunication in resource-limited environments.
As the Internet of Things (IoT) health monitoring systems expand, measures must be taken to ensure the safe,real-time handling of data, particularly for highly infectious diseases which limit the ability for human interaction.Blockchain provides the necessary transparency and immutability so that patient families and hospitals can align ontrust with health data. Hyperledger Fabric is the most commonly used solution for these scenarios, as it operates onX.509 certificates for the authentication of device identity. Yet, with the frequent transmission of the IoT sensors, theneed for repeated certificate authentication causes considerable computational lags, which can hinder critical real-timeassessments in the care continuum. We propose CAVIC (Certificate Authentication Verification Intelligent Cache),which is aimed at simplifying the verification and caching processes with the goal of diminishing the effects ofredundant cryptographic activities. CAVIC tracks the high-traffic IoT devices in a network, including temperature, heartrate, and oxygen saturation sensors, and creates a secured cache for their authenticated certificates. Further processesfrom these devices are verified via instant cache lookups rather than comprehensive certificate re-evaluations, whichdrastically reduce verification overhead. In situations where a patient's condition can worsen to an extent requiringimmediate action from a doctor, CAVIC guarantees that the system's time is dedicated to the analysis of patient datainstead of the continual validation of trusted sensors. Incorporating CAVIC into a Hyperledger-based healthcare testbed,we show reduced latency, lower computational costs, and better responsiveness, all of which add to the safety andefficiency of managing patients remotely.
Malware developers employ advanced API obfuscation techniques, such as name randomization, dynamicresolution, call stack manipulation, parameter spoofing, and API chaining, to bypass detection. Existing unimodalanalysis systems frequently fail to identify these threats due to their limited scope. To address this, we introduce a novelmulti-modal deep learning framework that combines temporal, structural, and parametric analysis for malware detection.This multi‑modal method uses a temporal encoder with self‑attention to detect name obfuscation and API chaining,Graph attention networks analyze call graphs for call stack tampering and dynamic API resolution, while a contrastivelearning module identifies anomalous parameter usage. A key novel approach is the dynamic fusion mechanism, whichuses attention-based weighting to combine features, enhancing accuracy and interpretability. Additionally, adversarialtraining ensures robustness against evasion attempts, with theoretical guarantees on performance under variations.Evaluated on 29,505 real‑world malware samples, the proposed multi‑modal framework achieves a 94.2% F1‑score (an18% improvement over unimodal baselines) and a 98.1% AUC‑ROC. The framework notably maintains 82%robustness against adversarial variations, significantly outperforming conventional LSTM‑based approaches (45%).Beyond detection, the proposed method provides explainable attention maps for forensic analysis and low latency(<1ms/sample), making it suitable for real-time security deployment. These results suggest that multi-modal fusion iscritical for next-generation endpoint protection.
The article examines the problem of ensuring the cyber resilience of critical information infrastructure in thecontext of the global transformation of cyberattack methods and the growing role of foreign state actors in destabilizingcritical systems. Given the unprecedented increase in the number of incidents and the emergence of new threat vectorsassociated with the use of artificial intelligence, traditional approaches to security assessment are becoming insufficient.This creates a critical need to develop tools for quantitatively calculating the level of critical information infrastructurecybersecurity in order to respond quickly to challenges and objectively predict risks. Based on a systematic analysis ofinternational standards and frameworks, such as the National Institute of Standards and Technology CybersecurityFramework, the article justifies the need for a formalized mathematical apparatus for the transition from a qualitativedescription of security measures to their quantitative measurement. The authors propose a hierarchical Multi-CriteriaDecision Making model for calculating an integral indicator of the level of cybersecurity. This model is based on asynthesis of the theoretical-multiple approach, the Analytic Hierarchy Process, and non-compensatory gating thresholds.The mathematical apparatus allows for the inequality of different groups of cybersecurity measures to be taken intoaccount by applying a system of weighting coefficients calculated on the basis of expert assessments using the Saatyscale. The development process includes eight distinct stages, from determining criteria sets to integrating noncompensatory gating mechanisms to prevent technical deficiencies from being masked by administrative achievements.Experimental testing of the model was conducted using a representative critical infrastructure facility grounded inanonymized real-world audit data from the energy sector. The results confirmed the model's effectiveness, yielding anintegral indicator of 0.56, while specifically identifying the detection domain as a vulnerable segment with a score of0.36. This confirms the practical significance of the model for supporting management decisions on priority resourceallocation.
The aim of the study is to quantitatively assess the execution time consistency of UI in distributed systems toidentify invariant risk profiles and degradation mechanisms and justify consistency-first, event-aware orchestrationi.The methodology integrates baseline-profiling, latency-, offline-replay- and concurrency-induction with event-leveliitelemetry, replication and non-parametric inference. The purpose is causal isolation of the degradation mechanisms forexecution time consistency of UI in distributed systems. It is empirically established that the execution time consistencyof UI in distributed client systems is an infrastructure invariant that systematically degrades under perturbations.Median shifts of ≈+6%, +9% and +13% ΔSDRiii were recorded, respectively, with large effect sizes (r≈0.57–1.00) forlatency, offline→replay, and concurrency. A stable risk profile of UI components was identified. It was maintained in65–85% of cases regardless of the type of failures and was explained by confirmation delays, merge conflicts, andcausal/order violations. Statistical verification confirmed the significance of all effects (p<0.01 after Holm–Bonferroni)with non-overlapping 95% bootstrap CIs, which indicates generalized non-artifact degradation and justifies thenecessity of event/state-aware orchestration. Further research prospects are the expansion of domains and multi-tenantarchitectures, the integration of causal tracing and idempotency auditing, and the combination of infrastructure and UXmetrics to reduce ΔSDR ≥30% in distributed scenarios.
