  Distributed link-flooding attacks constitute a new class of attacks with the
potential to segment large areas of the Internet. Their distributed nature
makes detection and mitigation very hard. This work proposes a novel framework
for the analytical modeling and optimal mitigation of such attacks. The
detection is modeled as a problem of relational algebra, representing the
association of potential attackers (bots) to potential targets. The analysis
seeks to optimally dissolve all but the malevolent associations. The framework
is implemented at the level of online Traffic Engineering (TE), which is
naturally triggered on link-flooding events. The key idea is to continuously
re-route traffic in a manner that makes persistent participation to
link-flooding events highly improbable for any benign source. Thus, bots are
forced to adopt a suspicious behavior to remain effective, revealing their
presence. The load-balancing objective of TE is not affected at all. Extensive
simulations on various topologies validate our analytical findings.
