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 anomaly detection transformer models, one per device. 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）的普及催生了众包感知应用，其中大量互联设备协同收集与分析数据。确保这些设备所收集数据的真实性与完整性，对于实现可靠决策及维护系统信任至关重要。传统认证方法易受攻击或易于复制，给保障众包感知应用安全带来挑战。此外，当前利用设备行为的解决方案主要聚焦于设备识别，而识别任务比认证更简单。为此，本文提出一种基于硬件行为指纹与Transformer自编码器的物联网设备个体认证框架。该方案利用IoT设备硬件固有的缺陷与差异，对具有相同规格的设备进行区分。通过监控与分析CPU、GPU、RAM及存储等关键硬件组件的行为，为每台设备创建唯一指纹。将性能样本视为时间序列数据，用于训练每台设备专属的异常检测Transformer模型。随后，该框架在基于Raspberry Pi设备的频谱众感知系统中得到验证。经过一系列实验，每台设备的模型均能在用于验证的45台设备中对其实现个体认证。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众包感知应用认证、安全性与可信度方面的有效性。