  Network robustness against attacks is one of the most fundamental researches
in network science as it is closely associated with the reliability and
functionality of various networking paradigms. However, despite the study on
intrinsic topological vulnerabilities to node removals, little is known on the
network robustness when network defense mechanisms are implemented, especially
for networked engineering systems equipped with detection capabilities. In this
paper, a sequential defense mechanism is firstly proposed in complex networks
for attack inference and vulnerability assessment, where the data fusion center
sequentially infers the presence of an attack based on the binary attack status
reported from the nodes in the network. The network robustness is evaluated in
terms of the ability to identify the attack prior to network disruption under
two major attack schemes, i.e., random and intentional attacks. We provide a
parametric plug-in model for performance evaluation on the proposed mechanism
and validate its effectiveness and reliability via canonical complex network
models and real-world large-scale network topology. The results show that the
sequential defense mechanism greatly improves the network robustness and
mitigates the possibility of network disruption by acquiring limited attack
status information from a small subset of nodes in the network.
