A Multitarget Backdoor Attack Against Automatic Modulation Recognition for IoT Wireless Signals

Deep learning-based automatic modulation recognition (AMR) is essential for enabling access authorization and spectrum management for interconnected devices and sensors in Internet of Things (IoT) systems. However, the open collection of data and the use of third-party training resources may introduce security vulnerabilities, especially backdoor attacks. Current research allows attackers to mislead receivers into misclassifying signals as a specific modulation type, but the fixed position of the trigger restricts its use in complex wireless networks. In this work, we propose a novel spatially distributed multi-target backdoor attack (SMBA) method. This method utilizes a trigger pattern to manipulate all types of input signals into multiple target modulation types by embedding the stealthy trigger at different spatial locations within the input signals. SMBA disseminates malicious samples across multiple target types specified by attackers, making it difficult for defenders to predict the target type into which the modulation signal embedded with the trigger is classified. This work reveals new security threats to AMR and provides important insights for developing defense technologies for IoT systems.