CyberMetrics is a next-generation cybersecurity organization focused on ML-driven SIEM solutions. We transform raw event logs into actionable intelligence, enabling SOC teams and threat hunters to detect, predict, and mitigate threats in real-time.

• 💻 AI-Powered SIEM: ML-driven detection, anomaly scoring, correlation.
• 📊 Behavioral Baselines: Learn normal activity and detect deviations.
• ⏱ Real-Time Detection: Instant alerting & automated threat scoring.
• 🛠 Open-Source Tooling: Modular pipelines for analytics and visualization.

• Objective: Anomaly detection in system logs using ML & LDA.
• Tech Stack: Python, Pandas, scikit-learn, LDA, XGBoost.
• Features: Behavioral baselines, ROC curves, SHAP explanations.
• Repo: Prototype-001

• Objective: Curated SIEM research & collaborative insights.
• Contents: Research papers, structured logs, team analysis.
• Repo: Research-Findings

• Languages: Python, SQL
• Libraries: Pandas, NumPy, scikit-learn, XGBoost, Matplotlib, Seaborn, Plotly, Gensim, NLTK
• Platforms: Jupyter Notebook, Google Colab
• ML Models: Random Forest, XGBoost, Autoencoders, LSTMs, LDA

CyberMetrics/
├── Projects/
│   ├── Prototype-001/
│   └── Research-Findings/
├── Data/                # Structured & anonymized logs
├── Docs/                # Research papers, whitepapers
├── Tools/               # ML pipelines & preprocessing scripts
├── Scripts/             # Automation helpers
└── README.md

1. Clone repository:

   git clone https://github.com/CyberMetrics/Prototype-001.git
   cd Prototype-001

2. Install dependencies:

   pip install -r requirements.txt
   # or install manually:
   pip install pandas numpy scikit-learn matplotlib seaborn gensim nltk plotly

3. Launch Notebook:

   jupyter notebook

4. Load logs:

   import pandas as pd
   mac_logs = pd.read_csv('data/Mac_2k.log_structured.csv')
   win_logs = pd.read_csv('data/Windows_2k.log_structured.csv')
   

• Fork & experiment with ML pipelines.
• Add new log formats or anomaly detection models.
• Improve visualizations & dashboards.
• Submit pull requests with enhancements.

• Allen Jose
• Satish Pakalapati
• Nikhil Reddie
• Prem Swaroop
• Chankapure Kameshwar
• Abijith Chowdary

MIT License — see LICENSE files in each repository.

• Open-source community: Pandas, NumPy, scikit-learn, Matplotlib, Seaborn, Plotly
• Academic research in SIEM & anomaly detection
• Security researchers and ML community