A TabPFN-based intrusion detection system for the industrial internet of things

Abstract

The industrial internet of things (IIoT) has undergone rapid growth in recent years, which has resulted in an increase in the number of threats targeting both IIoT devices and their connecting technologies. However, deploying tools to counter these threats involves tackling inherent limitations, such as limited processing power, memory, and network bandwidth. As a result, traditional solutions, such as the ones used for desktop computers or servers, cannot be applied directly in the IIoT, and the development of new technologies is essential to overcome this issue. One approach that has shown potential for this new paradigm is the implementation of intrusion detection system (IDS) that rely on machine learning (ML) techniques. These IDSs can be deployed in the industrial control system or even at the edge layer of the IIoT topology. However, one of their drawbacks is that, depending on the factory’s specifications, it can be quite challenging to locate sufficient traffic data to train these models. In order to address this problem, this study introduces a novel IDS based on the TabPFN model, which can operate on small datasets of IIoT traffic and protocols, as not in general much traffic is generated in this environment. To assess its efficacy, it is compared against other ML algorithms, such as random forest, XGBoost, and LightGBM, by evaluating each method with different training set sizes and varying numbers of classes to classify. Overall, TabPFN produced the most promising outcomes, with a 10–20% differentiation in each metric. The best performance was observed when working with 1000 training set samples, obtaining an F1 score of 81% for 6-class classification and 72% for 10-class classification.