Logs_Guard-AI

It is a unique, AI-driven DevOps tool designed for Linux environments. It uses machine learning to analyze server logs for anomaly detection, predicting potential failures before they occur. This predictive maintenance approach is rare in open-source tools, combining DevOps practices with AI to optimize Linux cluster management.

Project Structure

Logs_Guard-AI/
├── README.md
├── backend/
│   ├── app.py
│   ├── requirements.txt
│   ├── ml_model.py
│   ├── anomaly_detector.py
│   ├── utils.py
│   └── data_processor.py
├── frontend/
│   ├── package.json
│   ├── src/
│   │   ├── App.js
│   │   ├── index.js
│   │   ├── components/
│   │   │   ├── Dashboard.js
│   │   │   ├── LogUploader.js
│   │   │   └── PredictionResults.js
│   │   └── styles/
│   │       └── dashboard.css
│   └── public/
│       └── index.html
├── datasets/
│   ├── sample_logs/
│   │   ├── normal_log.txt
│   │   ├── anomaly_log_1.txt
│   │   ├── anomaly_log_2.txt
│   │   └── training_data.csv
├── scripts/
│   ├── ansible/
│   │   ├── playbook_fix_server.yml
│   │   └── inventory.ini
│   ├── docker/
│   │   ├── Dockerfile.backend
│   │   └── Dockerfile.frontend
│   └── k8s/
│       ├── deployment-backend.yaml
│       └── deployment-frontend.yaml
├── .github/
│   └── workflows/
│       └── ci-cd.yml
├── docs/
│   ├── architecture.md
│   └── user_guide.md
└── LICENSE

The tool features:

• Frontend: A modern React-based dashboard for uploading logs, viewing predictions, and triggering automated fixes.
• Backend: Python Flask API integrated with scikit-learn for ML-based anomaly detection.
• AI Component: A custom ML model trained on Linux syslog datasets to detect anomalies like high CPU spikes or disk errors.
• DevOps Integration: Ansible playbooks for automated remediation, Docker for containerization, and Kubernetes manifests for orchestration. CI/CD pipeline via GitHub Actions.
• Datasets: Sample Linux log files and a CSV for model training.

Why This?

• Focuses on predictive analytics for Linux-specific issues (e.g., kernel panics, SELinux errors), which is underrepresented in tools like Prometheus or ELK stack.
• Integrates AI not just for monitoring but for proactive IaC (Infrastructure as Code) suggestions via Ansible.
• Rare combination: ML-driven failure prediction with automated deployment fixes in a single repo.

Tech Stack

• Frontend: React.js with CSS for an attractive, responsive UI.
• Backend: Python 3.10+, Flask, scikit-learn.
• ML: Random Forest classifier for anomaly detection.
• DevOps: Ansible, Docker, Kubernetes, GitHub Actions.
• Data: Custom Linux log datasets.

Setup and Installation

Prerequisites

• Linux OS (tested on Ubuntu 22.04).
• Python 3.10+.
• Node.js 18+.
• Docker and Docker Compose.
• Minikube or Kubernetes cluster for full deployment.
• Ansible installed.

Quick Start

1. Clone the repo:

   git clone
   https://github.com/yourusername/Logs_Guard-AI.git
   cd AI-DevOps-Predicton
   

2. Backend Setup:

   cd backend python -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt
   python app.py
   

   Backend runs on http://localhost:5000.

3. Frontend Setup:

   cd ../frontend npm
   install npm start
   

   Frontend runs on http://localhost:3000.

4. Train ML Model:

   cd ../backend
   python ml_model.py
   

5. Docker Build:

   cd ../scripts/docker
   docker build -t backend-image -f Dockerfile.backend ../../backend
   docker build -t frontend-image -f Dockerfile.frontend ../../frontend
   

6. Kubernetes Deployment:

   kubectl apply -f ../../scripts/k8s/deployment-backend.yaml
   kubectl apply -f ../../scripts/k8s/deployment-frontend.yaml
   

7. Ansible Remediation (To automate server fixes):

    ansible-playbook -i scripts/ansible/inventory.ini scripts/ansible/playbook_fix_server.yml
   

Usage

• Upload Linux logs via the dashboard.
• View AI predictions on potential failures.
• Trigger Ansible fixes automatically.

Datasets

• datasets/sample_logs/: Contains normal and anomalous Linux syslogs.
• training_data.csv: Labeled data for ML training (features: CPU usage, error counts, etc.).

Architecture

See docs/architecture.md for detailed diagrams (text-based).

Contributing

Fork and PR! Ensure code is error-free and tested on Linux.

License

MIT License. See LICENSE.