Due to their computational efficiency and speed during training and inference, extreme learning machines are suitable for simple learning tasks on lightweight datasets. Examples of their real-world applications include healthcare and edge devices, where security concerns are crucial to be examined. Backdoor attacks are among the most common security threats against machine learning models but are almost completely unexplored for extreme learning machines.

This paper investigates the effects of backdoor attacks on extreme learning machines. First, we inject the backdoor into the model through data and model poisoning and then examine the pruning technique as a defense to defend against the attack. The core characteristic of extreme learning machines, which makes them interesting for study, is their different structure and learning procedure compared to deep neural networks. These features raise the question of whether they are as vulnerable to backdoor attacks as deep neural networks. Our experiments confirm this assumption and indicate that extreme learning machines can be backdoored with 100% attack success rate. Thus, we believe further study is needed to develop a robust defense technique as a solution to make them less vulnerable.

Backdoor attacks have been demonstrated as a security threat for machine learning models. Traditional backdoor attacks intend to inject backdoor functionality into the model such that the backdoored model will perform abnormally on inputs with predefined backdoor triggers and still retain state-of-the-art performance on the clean inputs. While there are already some works on backdoor attacks on Graph Neural Networks (GNNs), the backdoor trigger in the graph domain is mostly injected into random positions of the sample. There is no work analyzing and explaining the backdoor attack performance when injecting triggers into the most important or least important area in the sample, which we refer to as trigger-injecting strategies MIAS and LIAS, respectively. Our results show that, generally, LIAS performs better, and the differences between the LIAS and MIAS performance can be significant. Furthermore, we explain these two strategies’ similar (better) attack performance through explanation techniques, which results in a further understanding of backdoor attacks in GNNs.