Adaptive Neural Network Method for Detecting Crimes in the Digital Environment to Ensure Human Rights and Support Forensic Investigations

This article presents an adaptive neural network method for the automated detection, reconstruction, and prioritisation of multi-stage criminal operations in the digital environment, aiming to protect human rights and ensure the legal security of digital evidence. The developed method combines multimodal temporal encoders, a graph module based on GNN for entity correlation, and a correlation head with a link-prediction mechanism and differentiable path recovery. Sliding time windows, logarithmic transformation of volumetric features, and pseudonymization of identifiers with the ability to utilise privacy-preserving procedures (federated learning, differential privacy) are used for data aggregation and normalisation. Unique features of the developed method include an integrated risk function combining an anomaly component and graph significance, a module for automated forensic packet generation with chain of custody recording, and a mechanism for incremental model updates. Experimental results demonstrate high diagnostic metric values (AUC ≈ 0.97, F1 ≈ 0.99 on the test dataset after balancing), robust recovery of priority paths (“path_probability” > 0.7 for top operations), and pipeline performance in PII leak prioritisation and human trafficking reconstruction scenarios. The study’s contribution lies in a practice-oriented neural network method that integrates detection, correlation, and the collection of legally applicable evidence.