Actuators are essential components in cyber-physical systems, enabling system modules to perform diverse and complex tasks. Unfortunately, the pursuit of higher functional complexity often correlates with a broader attack surface in actuators. Thus, an efficient automated attack surface assessment is crucial to avoid cyber incidents in critical infrastructures. Limited by enormous parameter spaces, current methods rely on heuristic tests to evaluate interference potential but cannot thoroughly investigate the full spectrum of potential hidden interference. The observation that similar interference trigger configurations lead to the same impact has motivated us to use machine learning algorithms for understanding different impact samples around decision boundaries. By leveraging generalized knowledge of responses against specific attack scenarios, we aim to improve the efficiency of automated attack surface assessment of electromagnetic interference on new targets. To this end, we introduce EMFuzz, an automated mechanism to fuzz hardware to quantify varying adverse effects. We evaluate EMFuzz on 16 new servos within real-world scenarios, where it achieves an 86% accuracy in classifying different attack vectors. With the same test time, EMFuzz uncovers over twice the effective attack configurations of the baseline, greatly improving assessment efficiency. To further validate its efficacy, we apply EMFuzz to assess the attack surface of a new actuator from a robot transfer unit, and it can successfully reveal three distinct adverse effects.

The prevalence and large-scale uptake of the Internet of Things (IoT) have led to a growing trend of Identification Exploding, namely heterogeneous entities respectively identified to provide convenient intelligent services. To address challenges under Identification Exploding, unified modeling has become a promising approach to generalizing identification for heterogeneous entities in IoT. This paper surveys and discusses Identification Exploding's background to explore the possibility of unified modeling as one of the solutions. Meanwhile, challenges of realizing unified modeling are discussed. After that, comprehensive reviews on the latest modeling approaches and methods covering various IoT entities are carried out, including sensed entities and sensing devices. Special attention has been paid to modeling under resource-constrained IoT environments and relevant modeling-based industry solutions with critical analysis. It is proved that unified modeling is extremely significant under Identification Exploding Tendency in the IoT era.