Enhancing Network Resilience through Machine Learning-powered Graph
  Combinatorial Optimization: Applications in Cyber Defense and Information
  Diffusion
Diksha Goel
With the burgeoning advancements of computing and network communication
technologies, network infrastructures and their application environments have
become increasingly complex. Due to the increased complexity, networks are more
prone to hardware faults and highly susceptible to cyber-attacks. Therefore,
for rapidly growing network-centric applications, network resilience is
essential to minimize the impact of attacks and to ensure that the network
provides an acceptable level of services during attacks, faults or disruptions.
In this regard, this thesis focuses on developing effective approaches for
enhancing network resilience. Existing approaches for enhancing network
resilience emphasize on determining bottleneck nodes and edges in the network
and designing proactive responses to safeguard the network against attacks.
However, existing solutions generally consider broader application domains and
possess limited applicability when applied to specific application areas such
as cyber defense and information diffusion, which are highly popular
application domains among cyber attackers.
  This thesis aims to design effective, efficient and scalable techniques for
discovering bottleneck nodes and edges in the network to enhance network
resilience in cyber defense and information diffusion application domains. We
first investigate a cyber defense graph optimization problem, i.e., hardening
active directory systems by discovering bottleneck edges in the network. We
then study the problem of identifying bottleneck structural hole spanner nodes,
which are crucial for information diffusion in the network. We transform both
problems into graph-combinatorial optimization problems and design machine
learning based approaches for discovering bottleneck points vital for enhancing
network resilience.