# A Probabilistic Framework to Node-level Anomaly Detection in Communication Networks

arXiv: https://arxiv.org/abs/1902.04521 · HTML: https://mlsanity.com/paper/1902.04521/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2019.

## Built on by (most cited first, 2 of 2)

- [Multi-representations Space Separation based Graph-level Anomaly-aware Detection](https://mlsanity.com/paper/2307.12994/) (2023)
- [FadMan: Federated Anomaly Detection across Multiple Attributed Networks](https://mlsanity.com/paper/2205.14196/) (2022)

## Abstract (arXiv)

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.