Source: Cisco Talos Blog
Author: David J. Bianco
URL: https://blog.talosintelligence.com/introducing-evidenceforge-synthetic-security-logs-that-dont-look-as-fake/
ONE SENTENCE SUMMARY:
EvidenceForge generates realistic, causally consistent, multi-format synthetic security logs with ground truth, enabling training, detection validation, and scalable analytics development.
MAIN POINTS:
- High-quality labeled datasets are essential for training responders, validating detections, and building models.
- Production telemetry raises compliance issues, while public datasets are anonymized, stale, and over-reused.
- Self-generated attack simulations require real infrastructure, time, and scale poorly for scenario variety.
- Many synthetic generators emit independent events, breaking cross-source coherence and causal storytelling.
- EvidenceForge uses a canonical SecurityEvent model to synchronize fields across all emitters.
- Shared contexts enforce consistency for PIDs, LogonIDs, timestamps, and network identifiers like Zeek UIDs.
- Scenario YAML defines hosts, users, topology, and optional attack storylines for deterministic generation.
- Engine outputs 20+ correlated formats spanning Windows, Linux, network, and EDR telemetry.
- Rule engine inserts prerequisite protocol events with realistic timing for causal correctness.
- Background noise, red herrings, and bursty timing models improve realism and analyst training value.
TAKEAWAYS:
- Canonical event modeling solves the “logs don’t line up” problem across heterogeneous telemetry sources.
- Deterministic generation with seeded randomness enables repeatable datasets for regression testing detections.
- Sensor-placement modeling produces realistic network visibility gaps, mirroring real monitoring limitations.
- AI-assisted scenario authoring reduces expertise burden while scripts guarantee field-level consistency at scale.
- Companion ENVIRONMENT and GROUND_TRUTH documents provide analyst context and verifiable labels for evaluation.