The proliferation of the Internet of Things (IoT) has led to the emergence of crowdsensing applications, where a multitude of interconnected devices collaboratively collect and analyze data. Ensuring the authenticity and integrity of the data collected by these devices is crucial for reliable decision-making and maintaining trust in the system. Traditional authentication methods are often vulnerable to attacks or can be easily duplicated, posing challenges to securing crowdsensing applications. Besides, current solutions leveraging device behavior are mostly focused on device identification, which is a simpler task than authentication. To address these issues, an individual IoT device authentication framework based on hardware behavior fingerprinting and Transformer autoencoders is proposed in this work. This solution leverages the inherent imperfections and variations in IoT device hardware to differentiate between devices with identical specifications. By monitoring and analyzing the behavior of key hardware components, such as the CPU, GPU, RAM, and Storage on devices, unique fingerprints for each device are created. The performance samples are considered as time series data and used to train outlier detection transformer models, one per device and aiming to model its normal data distribution. Then, the framework is validated within a spectrum crowdsensing system leveraging Raspberry Pi devices. After a pool of experiments, the model from each device is able to individually authenticate it between the 45 devices employed for validation. An average True Positive Rate (TPR) of 0.74+-0.13 and an average maximum False Positive Rate (FPR) of 0.06+-0.09 demonstrate the effectiveness of this approach in enhancing authentication, security, and trust in crowdsensing applications.

翻译：物联网的普及催生了群智感知应用，其中大量互联设备协同采集与分析数据。确保这些设备所收集数据的真实性与完整性，对于实现可靠决策和维护系统信任至关重要。传统认证方法易受攻击或被轻易复制，给群智感知应用的安全保障带来挑战。此外，现有基于设备行为的解决方案多聚焦于设备识别（此任务较认证更为简单）。为解决上述问题，本文提出了一种基于硬件行为指纹和Transformer自动编码器的物联网设备个体认证框架。该方案利用物联网设备硬件固有的缺陷与差异，区分规格相同的设备。通过监测并分析设备关键硬件组件（如CPU、GPU、RAM和存储）的行为，为每台设备生成唯一指纹。将性能样本视为时序数据，用于训练每台设备的异常检测Transformer模型，该模型旨在建模其正常数据分布。随后，在基于树莓派设备的频谱群智感知系统中验证该框架。经多组实验，每台设备的模型能够在用于验证的45台设备中实现个体认证。平均真阳性率（TPR）为0.74±0.13，平均最大假阳性率（FPR）为0.06±0.09，证明了该方法在增强群智感知应用认证、安全与信任方面的有效性。