Abstract Due to its high data transmission rates, low power consumption, and excellent scalability, software-defined radio (SDR) has increasingly been applied in various fields, such as emergency communications and the internet of things. The successful deployment of SDR depends on the design and implementation of its security mechanisms. However, security threats may compromise fundamental functions through application management, data configuration, or transmission. This paper presents SDR’s basic architecture and security structure, analyzes its security requirements and threats, and elaborates on a series of SDR security detection and protection techniques, proposing several future development directions and challenges.

Algorithmic recourse (AR) has made significant progress by identifying small perturbations in input features that can alter predictions, which provide a data-centric approach to understand decisions from diverse black-box models on the Web. Towards the feasibility issue, i.e., whether the recoursed examples provides actionable and reliable recommendations to end-users, causal algorithmic recourse have incorporated structural causal model (SCM) to preserve the realistic constraints among input features. For instance, preserving structural causal knowledge between "age" and "educational level" can avoid generating samples with decreasing age and increasing educational level. However, previous causal AR methods suffer from the requirement of prior structural causal knowledge, e.g., prior causal graph or the whole SCM, which restricts the realistic application of causal AR methods.