Mapping Attack Paths in Black-Box Networks Through Passive Vulnerability Inference

Abstract

This project investigates stealthy techniques for mapping attack graphs through black- box networks. This provides a powerful new capability for network reconnaissance and attack planning, when open scanning is not an option. We employ purely passive inference, as well as new hybrid passive/active techniques that provide more comprehensive attack plans while maintaining nearly zero risk of detection. We infer network configuration (topology, devices, services, etc.), as well as functional semantics of network components for intelligent targeting. We map discovered network elements to potentially exploitable vulnerabilities.

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Document Details

Document Type
Technical Report
Publication Date
Aug 30, 2011
Accession Number
ADA563714

Entities

People

  • Anup Ghosh
  • Steve Noel
  • Sushil Jajodia

Organizations

  • George Mason University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Application Protocols
  • Change Detection
  • Computers
  • Computing System Architectures
  • Data Links
  • Deployment
  • Detection
  • Information Systems
  • Internet
  • Intrusion Detection
  • Intrusion Detection Systems
  • Intrusion Detectors
  • Network Protocols
  • Network Topology
  • Operating Systems
  • Web Browsers

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Neural Networks