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(IoT)的普及催生了群智感知应用的涌现，此类应用通过大量互联设备协同采集与分析数据。确保这些设备所采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。传统身份验证方法常易受攻击或可轻易被复制，这对保障群智感知应用的安全构成了挑战。此外，当前利用设备行为特征的解决方案主要聚焦于设备识别，而识别任务本身比认证更为简单。针对上述问题，本文提出了一种基于硬件行为指纹和Transformer自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性，区分具有相同规格的不同设备。通过监测并分析设备关键硬件组件（如CPU、GPU、RAM及存储）的行为模式，为每台设备创建独特的指纹特征。将硬件性能采样数据视作时间序列，用于训练每台设备专属的离群点检测Transformer模型，以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。经过系列实验，每台设备的模型能够在45台验证设备中实现个体认证。平均真正率(TPR)为0.74±0.13，平均最大假正率(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性、防护能力与信任度方面的有效性。