Uncovering and Understanding FPR Manipulation Attack in Industrial IoT Networks

TL;DR

This paper reveals a novel FPR manipulation attack on industrial IoT network intrusion detection systems, exploiting domain knowledge and packet perturbations to significantly increase false positives.

Contribution

It introduces a practical attack method that manipulates benign traffic labels without traditional techniques, demonstrating high success rates and impact on alert investigation delays.

Findings

FPA achieves 80.19% to 100% success rate.

Small false positive fractions delay genuine alerts by up to 2 hours.

Adversarial training can improve model robustness against FPA.

Abstract

In the network security domain, due to practical issues -- including imbalanced data and heterogeneous legitimate network traffic -- adversarial attacks in machine learning-based NIDSs have been viewed as attack packets misclassified as benign. Due to this prevailing belief, the possibility of (maliciously) perturbed benign packets being misclassified as attack has been largely ignored. In this paper, we demonstrate that this is not only theoretically possible, but also a particular threat to NIDS. In particular, we uncover a practical cyberattack, FPR manipulation attack (FPA), especially targeting industrial IoT networks, where domain-specific knowledge of the widely used MQTT protocol is exploited and a systematic simple packet-level perturbation is performed to alter the labels of benign traffic samples without employing traditional gradient-based or non-gradient-based methods. The experimental evaluations demonstrate that this novel attack results in a success rate of 80.19% to 100%. In addition, while estimating impacts in the Security Operations Center, we observe that even a small fraction of false positive alerts, irrespective of different budget constraints and alert traffic intensities, can increase the delay of genuine alerts investigations up to 2 hr in a single day under normal operating conditions. Furthermore, a series of relevant statistical and XAI analyses is conducted to understand the key factors behind this remarkable success. Finally, we explore the effectiveness of the FPA packets to enhance models' robustness through adversarial training and investigate the changes in decision boundaries accordingly.