Heat-ray: combating identity snowball attacks using machinelearning, combinatorial optimization and attack graphs

Abstract

As computers have become ever more interconnected, the complexity of security configuration has exploded. Management tools have not kept pace, and we show that this has made identity snowball attacks into a critical danger. Identity snowball attacks leverage the users logged in to a first compromised host to launch additional attacks with those users' privileges on other hosts. To combat such attacks, we present Heat-ray, a system that combines machine learning, combinatorial optimization and attack graphs to scalably manage security configuration. Through evaluation on an organization with several hundred thousand users and machines, we show that Heat-ray allows IT administrators to reduce by 96% the number of machines that can be used to launch a large-scale identity snowball attack.