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https://github.com/yaron4u/sentinelfusion
An advanced threat hunting platform that combines the power of network monitoring, log analysis, and machine learning to proactively identify and respond to cyber threats.
https://github.com/yaron4u/sentinelfusion
analysis anomaly-detection cybersecurity integration meachinelearning network soar threat-hunting
Last synced: 3 months ago
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An advanced threat hunting platform that combines the power of network monitoring, log analysis, and machine learning to proactively identify and respond to cyber threats.
- Host: GitHub
- URL: https://github.com/yaron4u/sentinelfusion
- Owner: yaron4u
- License: other
- Created: 2023-05-18T21:04:55.000Z (over 1 year ago)
- Default Branch: beta
- Last Pushed: 2023-09-26T18:16:43.000Z (over 1 year ago)
- Last Synced: 2024-10-12T20:34:21.630Z (4 months ago)
- Topics: analysis, anomaly-detection, cybersecurity, integration, meachinelearning, network, soar, threat-hunting
- Language: Python
- Homepage:
- Size: 19.5 KB
- Stars: 4
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# SentinelFusion
## Introduction
SentinelFusion is a robust cybersecurity tool that combines network monitoring, log analysis, and machine learning to proactively identify and respond to cyber threats. The platform features real-time network traffic analysis, log aggregation, threat intelligence integration, anomaly detection, alerting, visualization, and a streamlined incident response workflow.
# Features (will be added)
Real-time Network Traffic Analysis
The platform includes a network traffic analysis tool that captures and analyzes network packets in real-time. Python and libraries like Scapy or dpkt are used to extract information such as IP addresses, protocols, and payload data.
## Log Aggregation and Analysis
This feature aggregates logs from various sources including firewalls, servers, and network devices. Bash scripting is used to automate log collection and parsing, extracting relevant information for analysis.
## Threat Intelligence Integration
SentinelFusion integrates with external threat intelligence feeds to enrich the analysis process. It retrieves information about known malicious IP addresses, domains, and signatures to identify potential threats.
## Anomaly Detection
Machine learning algorithms, such as clustering or anomaly detection, are used to identify abnormal behavior or patterns in network traffic and log data. Models are trained on historical data and continuously updated for accurate threat detection.
## Alerting and Visualization
An alerting system is included that triggers notifications when suspicious activities are detected. Python libraries like Flask or Django are used to develop a web-based dashboard that provides real-time visualization of network and security events.
## Incident Response Workflow
SentinelFusion features an incident response workflow module that facilitates the investigation and response process. It provides case management, evidence collection, and collaboration tools to streamline incident handling.
## Threat Hunting Playbooks
Predefined threat hunting playbooks are included to guide analysts in investigating specific types of threats or attack scenarios. These playbooks include step-by-step instructions, queries, and tools to assist in detection and mitigation.
## Integration with SOAR Platforms
SentinelFusion integrates with Security Orchestration, Automation, and Response (SOAR) platforms, enabling automated incident response actions based on predefined rules or triggers.
## Reporting and Forensics
SentinelFusion generates detailed reports on identified threats, attack vectors, and recommended countermeasures. Additional tools and scripts are provided for digital forensics, allowing analysts to perform deeper investigations when necessary.
## Continuous Improvement
A feedback loop is implemented within the platform to learn from detected threats and improve future detection capabilities. This involves analyzing false positives and false negatives to refine detection algorithms and enhance overall accuracy.
## Conclusion
SentinelFusion showcases expertise in cybersecurity, networks, data analysis, machine learning, scripting, and building scalable platforms. It provides a comprehensive solution for proactive threat hunting, with the ability to detect and respond to advanced threats.
## SentinelFusion Flow Diagram
