SNFOK

Security & Forensic of Kubernetes

Overview

This project focuses on enhancing the security and forensic capabilities of Kubernetes environments by leveraging continual deep learning. The system detects, analyzes, and mitigates cyber threats within Kubernetes clusters, ensuring robust protection against modern attacks.

Key Features

• Threat Detection: Utilizes continual deep learning models to identify anomalies and potential threats in real-time.
• Forensic Analysis: Provides detailed analysis of detected incidents, helping to understand the root cause.
• Kubernetes Integration: Seamlessly integrates with Kubernetes clusters without disrupting operations.
• Automation: Automates threat detection and response processes to reduce manual effort.
• Scalability: Designed to handle Kubernetes environments of varying sizes, from small clusters to large enterprise setups.

Architecture

The system consists of

1. Data Collector: Gathers logs and metrics from Kubernetes components and applications.
2. Preprocessing Module: Filters, normalizes, and prepares the data for analysis.
3. Deep Learning Engine: A continual learning model that detects anomalies and identifies threats.
4. Alerting & Response Module: Notifies administrators of threats and optionally executes predefined mitigation actions.
5. Forensic Dashboard: Provides a user-friendly interface for analyzing incidents.

Technologies Used

• Programming Language:
• Deep Learning Framework:
• Kubernetes Tools:
• Queue:
• Databases:
• Visualization:

License

This project is licensed under the GPL-3.0 License.

Contact

For queries or support, please reach out to info@hashx.tech.