Anomaly-based mitigation of access request risk
US12381876B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 22, 2021 |
| Grant date | Aug 5, 2025 |
| Priority date | — |
| Expiry date | Jun 13, 2042 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L63/1425
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Access to secured items in a computing system is requested instead of being persistent. Access requests may be granted on a just-in-time basis. Anomalous access requests are detected using machine learning models based on historic patterns. Models utilizing conditional probability or collaborative filtering also facilitate the creation of human-understandable explanations of threat assessments. Individual machine learning models are based on historic data of users, peers, cohorts, services, or resources. Models may be weighted, and then aggregated in a subsystem to produce an access request risk score. Scoring principles and conditions utilized in the scoring subsystem may include probabilities, distribution entropies, and data item counts. A feedback loop allows incremental refinement of the subsystem. Anomalous requests that would be automatically approved under a policy may instead face human review, and low threat requests that would have been delayed by human review may instead be approved automatically.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.