  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.
