The problem of multi-criteria selection of network security configurations (NSC) under resource constraints and the necessity to comply with information security (IS) policies is addressed in this study. A formal mathematical model of the problem has been developed, encompassing the definition of a set of possible security mechanism configurations, the formalization of objective functions reflecting security levels, throughput, and deployment costs, and the introduction of constraints on feasible solutions. The NSGA-II (Non-dominated Sorting Genetic Algorithm II) optimization algorithm is employed to generate a set of Pareto-optimal solutions, ensuring uniform coverage of compromise configurations. A software package implemented in Python 3 incorporates modules for population generation, fitness evaluation, selection, crossover, mutation operators, and result visualization. Computational experiments (CE) were conducted to validate the effectiveness of the proposed approach. The evolution dynamics of the Pareto hypervolume were analyzed, the uniformity of solution distribution in the objective space was studied, and the impact of algorithm parameters on convergence to the optimal solution was examined. The results demonstrate that the proposed methodology enables the formation of NSC sets that achieve a balanced trade-off between security, throughput, and IS system deployment costs.

Large Language Model (LLM)-driven Secure Shell (SSH) honeypots have rapidly displaced static, templated decoys, yet they share a structural limitation: a single model and a single policy serve every turn of an intrusion, from low-stakes reconnaissance to high-value persistence. This review asks whether honeypots can instead adapt their inference to the attacker’s phase. We conduct a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020-guided systematic mapping review of fifty influential studies (2020–2026) drawn from three strands—honeypots and generative deception, cost-aware model routing and mixture-of-experts inference, and LLM agents, embeddings, and retrieval-augmented generation in security operations—assembled under a disclosed, purposive top-50 prioritization ranked by age-normalized citation velocity. That prioritization is a deliberate mapping strategy: it surfaces the field’s most influential work rather than claiming exhaustive coverage, and a sensitivity analysis tests how robust the conclusions are to the cap. Three findings emerge. Across the reviewed literature, honeypot adaptivity has advanced at the policy and generation layers but not at the inference-allocation layer; the routing literature has solved capacity allocation but conditions only on query-intrinsic signals, not on adversarial session phase; and embedding-based analytics, corroborated by a production deployment, show the phase signal is observable and reliable. The strands are individually mature and mutually disconnected. We formalize this gap and propose a phase-adaptive, agent-oriented framework that pairs an embedding-based phase estimator and an O(1) phase-to-model dispatch layer with an unprivileged, campaign-level observation substrate.The review contributes a reproducible corpus, an age-normalized bibliometric ranking, a formal gap analysis, and a conceptual architecture that operationalizes the maturation arc from static to phase-adaptive honeypots.

Public cattle re-identification benchmarks remain limited in scale and rarely provide leakage-controlled open-set and cross-camera evaluation. Using 25,209 curated crops of 197 Holstein cows recorded by four synchronized side-view cameras in a passageway of a working farm, we investigate viewpoint-related shifts in the embedding space and evaluate cattle re-identification under camera-disjoint gallery/query construction. The extractor combines an animal-pretrained MegaDescriptor Swin-B backbone with multi-level projections, learned gating, quality weighting, and two-level aggregation. Under the strictly inductive identity-disjoint cross-camera protocol without camera centering, the proposed model achieved Rank-1 of 55.7% (95% CI: 47.7–63.8%), Rank-5 of 90.0% (86.3–93.7%), and mAP of 70.2% (66.3–74.1%). Under the same protocol, TransReID ViT-B/16 achieved 38.6%, 77.9%, and 55.9%, while OSNet-x1.0 achieved 12.9%, 47.9%, and 30.7%, respectively. The comparison with the pure-GAP configuration was not statistically resolved (Holm-adjusted p = 0.91). In held-out open-set evaluation, the validation-selected global cosine threshold obtained the strongest mean AUROC, F1, and AUOSCR (63.2%, 66.9%, and 44.4%); the corresponding results were 60.2%, 64.4%, and 38.8% for Mahalanobis rejection and 56.9%, 63.2%, and 41.7% for EVT. Zero-shot evaluation at a second corridor on the same farm yielded Rank-1 of 26.6%, Rank-5 of 41.9%, and mAP of 35.3%. These results establish cross-camera feasibility under the defined protocols and same-farm cross-installation transfer, but do not establish cross-farm, cross-device, seasonal, longitudinal, or full production robustness.

JAIT is a scientific open access journal which focuses on empirical research results and critical analysis of technology development, use, management and impacts in information technology.

This paper presents a set of algorithms and models for the comprehensive assessment of the environmental situation in urbanised areas subject to anthropogenic impacts from industrial and transport facilities. The proposed mechanism combining fuzzy logic and neural network predictive models, with subsequent spatial visualization of the results using GIS technologies, enabling a shift from traditional component-by-component assessments to a holistic spatio-temporal characterisation of the environmental situation, with the possibility of formulating well-founded management measures. An algorithm for generating management scenarios has been developed, ensuring a closed-loop cycle: monitoring—integrated assessment—forecasting—development of environmental protection measures. The algorithm was tested using field environmental monitoring data from urban areas, collected at 82 road sections (2023–2025), for which motor vehicle traffic was identified as the primary source of anthropogenic pressure. Forty-three mathematical models were developed based on an artificial neural network, which achieved a coefficient of determination of 0.75–0.82 on a test data sample. Spatial visualisation of the results was implemented in the ArcMap 10.8 GIS system, whilst modelling was carried out in the MatLab environment. The objective of this study is to develop a set of tools for a comprehensive assessment of the environmental conditions in urbanized areas. The proposed set of tools can be expanded to include other components, however, testing is currently limited to two components (CO and noise) in a single city (Oryol, Russia).