# Efficient Passive ICS Device Discovery and Identification by MAC Address Correlation

## TL;DR

This paper introduces a lightweight passive network monitoring method that uses MAC address correlation to identify industrial devices, achieving high discovery and identification rates without active scanning.

## Contribution

It presents a novel passive device discovery technique based on MAC address correlation, outperforming existing tools in industrial network environments.

## Key findings

- Achieved 100% host discovery rate in testbed.
- More than 66% device/vendor identification accuracy.
- Outperformed existing passive scanning tools.

## Abstract

Owing to a growing number of attacks, the assessment of Industrial Control Systems (ICSs) has gained in importance. An integral part of an assessment is the creation of a detailed inventory of all connected devices, enabling vulnerability evaluations. For this purpose, scans of networks are crucial. Active scanning, which generates irregular traffic, is a method to get an overview of connected and active devices. Since such additional traffic may lead to an unexpected behavior of devices, active scanning methods should be avoided in critical infrastructure networks. In such cases, passive network monitoring offers an alternative, which is often used in conjunction with complex deep-packet inspection techniques. There are very few publications on lightweight passive scanning methodologies for industrial networks. In this paper, we propose a lightweight passive network monitoring technique using an efficient Media Access Control (MAC) address-based identification of industrial devices. Based on an incomplete set of known MAC address to device associations, the presented method can guess correct device and vendor information. Proving the feasibility of the method, an implementation is also introduced and evaluated regarding its efficiency. The feasibility of predicting a specific device/vendor combination is demonstrated by having similar devices in the database. In our ICS testbed, we reached a host discovery rate of 100% at an identification rate of more than 66%, outperforming the results of existing tools.