LogAtlas-Defense-Set 🛡️🦊
A heterogeneous, labeled log dataset designed for training and evaluating log-level and session-level classifiers that distinguish between normal behavior and cyberattacks across multiple sources (system, network, and application logs). It is intended as the “defense layer” of the LogAtlas ecosystem, focusing on robust, realistic attack detection under varied class distributions.
Mascot
The LogAtlas-Defense-Set mascot is a vigilant cyber guardian fox in lightweight futuristic armor, standing on a shield made of interlocking log fragments with threat heatmaps. Its glowing tail traces highlight red “attack” regions in the log landscape, symbolizing precise and efficient detection of malicious activity in heterogeneous logs.
Dataset Overview
- Goal: Supervised training and evaluation of models that classify log sessions as normal or attack and support downstream severity-aware analysis.
- Scope: Multi-source logs (e.g., system, network, application) organized into sessions with rich metadata, including attack ratios and severity labels.
- Use cases:
- Training LLM-based or transformer-based detectors
- Benchmarking attack detection under different class distributions
- Studying severity-aware or source-aware threat modeling