01 実世界データ収集

CyberSense uses anonymized customer data, academia, and third-party sources to compile millions of data samples and file operations. This enables our models to learn from diverse scenarios, not synthetic testbeds.

02 ライブランサムウェアデトネーション

In a secure lab environment, we’ve detonated thousands of ransomware variants to observe the true impact of malicious changes to data. This forms the foundation for accurate pattern recognition to distinguish between clean and infected data.

03 自動データセット生成

Using both the detonated and clean samples, the lab uses our native indexing and processing capabilities to generate millions of virtual backups and attack scenarios. These are then classified by behavioral indicators of compromise and used to train and continuously refine our detection models.

04 Rigorous Testing & Validation

Each model is trained and refined through an iterative cycle of testing, using real-world samples and simulated attacks. Models are validated to both meet our SLA and ensure resilience in the field.

The result? 99.99% detection accuracy based on validation across 125,000 real-world samples.