  Cyberthreats are a permanent concern in our modern technological world. In
the recent years, sophisticated traffic analysis techniques and anomaly
detection (AD) algorithms have been employed to face the more and more
subversive adversarial attacks. A malicious intrusion, defined as an invasive
action intending to illegally exploit private resources, manifests through
unusual data traffic and/or abnormal connectivity pattern. Despite the plethora
of statistical or signature-based detectors currently provided in the
literature, the topological connectivity component of a malicious flow is less
exploited. Furthermore, a great proportion of the existing statistical
intrusion detectors are based on supervised learning, that relies on labeled
data. By viewing network flows as weighted directed interactions between a pair
of nodes, in this paper we present a simple method that facilitate the use of
connectivity graph features in unsupervised anomaly detection algorithms. We
test our methodology on real network traffic datasets and observe several
improvements over standard AD.
