Farm-flow dataset: Intrusion detection in smart agriculture based on network flows

Abstract

In recent years, the Internet of Things (IoT) revolutionized agricultural management by enabling data-driven decision-making through seamless connectivity among various devices and equipment. The security of Agricultural IoT (AG-IoT) devices becomes increasingly evident as reliance on them grows. On the other hand, machine learning models for intrusion detection show promise in identifying vulnerabilities, but their effectiveness depends on being trained on representative data. Indeed, there is a notable gap in network intrusion detection for AG-IoT, as existing datasets for training machine learning models lack the context of AG-IoT scenarios. Also, most existing ones rely on packed-based features (and not on network flow data), and analysing such data can be resource-consuming.

In this work, we present the “Farm-Flow” dataset. We created a realistic AG-IoT scenario to build the dataset and executed eight types of network attacks. Over one million instances of relevant data were collected, which we combined into network flows, organized and made publicly available via http://doi.org/10.5281/zenodo.10964647.

The dataset created has been evaluated using multiple intrusion detection models in terms of their capabilities to identify and classify malicious traffic. The assessed models presented high performance and even achieved an F1-score of more than 90% while identifying malicious traffic. The “Farm-Flow” may support the training of intrusion detection methods, and the performance results contribute to future benchmarking.