A Probabilistic Framework to Node-level Anomaly Detection in Communication Networks
This work addresses node-level anomaly detection in communication networks, which is an incremental improvement with specific applications in sensor networks.
The paper tackles the problem of detecting abnormal communication volume at node-level in networks by modeling communication as clique streams and using non-parametric regression to estimate participation probabilities, achieving superior performance in real-world and synthetic data.
In this paper we consider the task of detecting abnormal communication volume occurring at node-level in communication networks. The signal of the communication activity is modeled by means of a clique stream: each occurring communication event is instantaneous and activates an undirected subgraph spanning over a set of equally participating nodes. We present a probabilistic framework to model and assess the communication volume observed at any single node. Specifically, we employ non-parametric regression to learn the probability that a node takes part in a certain event knowing the set of other nodes that are involved. On the top of that, we present a concentration inequality around the estimated volume of events in which a node could participate, which in turn allows us to build an efficient and interpretable anomaly scoring function. Finally, the superior performance of the proposed approach is empirically demonstrated in real-world sensor network data, as well as using synthetic communication activity that is in accordance with that latter setting.