# Temporal graph-based approach for behavioural entity classification

arXiv: https://arxiv.org/abs/2105.04798 · HTML: https://mlsanity.com/paper/2105.04798/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 4 of 4)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Graph-based Anomaly Detection and Description: A Survey](https://mlsanity.com/paper/1404.4679/) (2014)
- [Graph Convolutional Network-based Suspicious Communication Pair Estimation for Industrial Control Systems](https://mlsanity.com/paper/2007.10204/) (2020)

## Built on by (most cited first, 1 of 1)

- [NBcoded: network attack classifiers based on Encoder and Naive Bayes model for resource limited devices](https://mlsanity.com/paper/2109.07273/) (2021)

## Abstract (arXiv)

Graph-based analyses have gained a lot of relevance in the past years due to their high potential in describing complex systems by detailing the actors involved, their relations and their behaviours. Nevertheless, in scenarios where these aspects are evolving over time, it is not easy to extract valuable information or to characterize correctly all the actors. In this study, a two phased approach for exploiting the potential of graph structures in the cybersecurity domain is presented. The main idea is to convert a network classification problem into a graph-based behavioural one. We extract these graph structures that can represent the evolution of both normal and attack entities and apply a temporal dissection approach in order to highlight their micro-dynamics. Further, three clustering techniques are applied to the normal entities in order to aggregate similar behaviours, mitigate the imbalance problem and reduce noisy data. Our approach suggests the implementation of two promising deep learning paradigms for entity classification based on Graph Convolutional Networks.