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Data Exfiltration Detection Engine

A security-focused endpoint monitoring and analysis platform designed to detect, investigate, and report unauthorized outbound data transfers.

The project aims to identify which processes are sending data over the internet, determine where the data is being sent, analyze transfer behavior, and detect potential data exfiltration attempts.

Overview

Data exfiltration is the unauthorized transfer of information from a system to an external destination.

This project focuses on:

  • Process monitoring
  • Network activity analysis
  • Process-to-network correlation
  • Outbound data tracking
  • Suspicious behavior detection
  • Security alert generation

The goal is to provide visibility into endpoint network activity and help identify processes that may be transferring sensitive data outside the system.

Objectives

  • Monitor outbound network activity
  • Attribute network connections to specific processes
  • Analyze data transfer behavior
  • Detect suspicious upload patterns
  • Generate security alerts
  • Build process activity timelines
  • Support incident investigation workflows

Features

Process Discovery

Enumerate active processes running on the endpoint.

Example:

PID: 4120
Process: python.exe

Network Connection Monitoring

Monitor active network connections.

Track:

  • Local Address
  • Remote Address
  • Remote Port
  • Protocol
  • Connection State

Example:

Process:
python.exe

Destination:
104.18.x.x

Protocol:
TCP

Port:
443

Process-to-Network Correlation

Associate network activity with the responsible process.

Example:

python.exe
↓
Connected To
↓
example.com

Data Transfer Analysis

Measure:

  • Upload volume
  • Download volume
  • Transfer rate
  • Session statistics

Example:

Process:
python.exe

Uploaded:
850 MB

Downloaded:
12 MB

Connection Intelligence

Collect contextual information about network destinations.

Track:

  • IP Address
  • Domain Name
  • Connection History
  • Frequency of Communication

Example:

Destination:
api.example.com

Connections:
57

Process Behavior Timeline

Build a chronological view of process activity.

Example:

10:01 Process Started

10:03 Network Connection Opened

10:04 Upload Activity Detected

10:10 Connection Closed

Suspicious Activity Detection

Detect potentially abnormal behaviors.

Examples:

  • Excessive outbound transfers
  • Repeated connections to unknown destinations
  • Continuous background uploads
  • Unexpected network activity

Example:

Alert

Process:
python.exe

Reason:
Unusual outbound transfer volume

Security Alerting

Generate alerts for:

  • High upload volume
  • Suspicious communication patterns
  • Unknown process activity
  • Potential data exfiltration events

Incident Reporting

Create investigation reports containing:

  • Process information
  • Network destinations
  • Data transfer statistics
  • Event timeline
  • Detection findings

Architecture

Processes
    │
    ▼
Process Monitor
    │
    ▼
Network Monitor
    │
    ▼
Correlation Engine
    │
    ▼
Behavior Analysis Engine
    │
    ▼
Detection Engine
    │
    ▼
Alerting & Reporting

Roadmap

M1 — Network Monitoring Foundation

  • Active connection enumeration
  • Network activity collection
  • Connection tracking

M2 — Process Attribution Engine

  • Process discovery
  • PID tracking
  • Process-to-connection mapping

M3 — Connection Intelligence

  • Destination analysis
  • Domain resolution
  • Communication statistics

M4 — Data Transfer Analysis

  • Upload monitoring
  • Download monitoring
  • Transfer metrics

M5 — Process Behavior Timeline

  • Event collection
  • Activity timeline generation
  • Historical analysis

M6 — Suspicious Behavior Detection

  • Behavioral rules
  • Anomaly detection
  • Alert generation

M7 — Data Exfiltration Detection Engine

  • Multi-event correlation
  • Exfiltration pattern identification
  • Investigation support

M8 — Alerting System

  • Security notifications
  • Detection alerts
  • Incident creation

M9 — Response Engine

  • Process isolation
  • Connection blocking
  • Automated response workflows

M10 — Mini EDR Foundation

  • Endpoint monitoring
  • Threat detection
  • Security analytics
  • Incident response

Technologies

Planned Technologies:

  • C++
  • Windows API
  • Winsock
  • Multithreading
  • Event Logging
  • Security Telemetry

Target Users

  • Security Software Engineers
  • Endpoint Security Engineers
  • Detection Engineers
  • Security Researchers
  • SOC Analysts
  • Incident Responders

Long-Term Vision

Build a lightweight endpoint security platform capable of:

  • Monitoring endpoint activity
  • Detecting unauthorized outbound communications
  • Identifying potential data exfiltration attempts
  • Supporting threat investigation and incident response

through process-aware network intelligence and behavioral analysis.

License

License to be determined.