Identification of Device Dependencies Using Link Prediction

TL;DR

This paper presents a novel graph-based machine learning approach for automatically identifying device dependencies in large, dynamic computer networks by using link prediction with IP address embeddings derived from constrained random walks.

Contribution

The paper introduces a new method leveraging neural network-based IP address embeddings and constrained random walks for dependency detection, applicable in real-time network analysis.

Findings

Achieves acceptable accuracy in dependency identification

Handles all dependency types simultaneously

Suitable for batch processing in operational networks

Abstract

Devices in computer networks cannot work without essential network services provided by a limited count of devices. Identification of device dependencies determines whether a pair of IP addresses is a dependency, i.e., the host with the first IP address is dependent on the second one. These dependencies cannot be identified manually in large and dynamically changing networks. Nevertheless, they are important due to possible unexpected failures, performance issues, and cascading effects. We address the identification of dependencies using a new approach based on graph-based machine learning. The approach belongs to link prediction based on a latent representation of the computer network's communication graph. It samples random walks over IP addresses that fulfill time conditions imposed on network dependencies. The constrained random walks are used by a neural network to construct IP address embedding, which is a space that contains IP addresses that often appear close together in the same communication chain (i.e., random walk). Dependency embedding is constructed by combining values for IP addresses from their embedding and used for training the resulting dependency classifier. We evaluated the approach using IP flow datasets from a controlled environment and university campus network that contain evidence about dependencies. Evaluation concerning the correctness and relationship to other approaches shows that the approach achieves acceptable performance. It can simultaneously consider all types of dependencies and is applicable for batch processing in operational conditions.