A Survey of MulVAL Extensions and Their Attack Scenarios Coverage

08/11/2022
by   David Tayouri, et al.
0

Organizations employ various adversary models in order to assess the risk and potential impact of attacks on their networks. Attack graphs represent vulnerabilities and actions an attacker can take to identify and compromise an organization's assets. Attack graphs facilitate both visual presentation and algorithmic analysis of attack scenarios in the form of attack paths. MulVAL is a generic open-source framework for constructing logical attack graphs, which has been widely used by researchers and practitioners and extended by them with additional attack scenarios. This paper surveys all of the existing MulVAL extensions, and maps all MulVAL interaction rules to MITRE ATT CK Techniques to estimate their attack scenarios coverage. This survey aligns current MulVAL extensions along unified ontological concepts and highlights the existing gaps. It paves the way for methodical improvement of MulVAL and the comprehensive modeling of the entire landscape of adversarial behaviors captured in MITRE ATT CK.

READ FULL TEXT

page 8

page 16

research
06/24/2019

Extending Attack Graphs to Represent Cyber-Attacks in Communication Protocols and Modern IT Networks

An attack graph is a method used to enumerate the possible paths that an...
research
10/08/2015

Exact Inference Techniques for the Analysis of Bayesian Attack Graphs

Attack graphs are a powerful tool for security risk assessment by analys...
research
02/28/2023

A Survey of Automatic Generation of Attack Trees and Attack Graphs

Graphical security models constitute a well-known, user-friendly way to ...
research
06/18/2013

Attack Planning in the Real World

Assessing network security is a complex and difficult task. Attack graph...
research
10/06/2021

A Novel Approach for Attack Tree to Attack Graph Transformation: Extended Version

Attack trees and attack graphs are both common graphical threat models u...
research
06/22/2016

Efficient Attack Graph Analysis through Approximate Inference

Attack graphs provide compact representations of the attack paths that a...
research
03/10/2020

A Survey of Adversarial Learning on Graphs

Deep learning models on graphs have achieved remarkable performance in v...

Please sign up or login with your details

Forgot password? Click here to reset