# On Evaluating IoT Data Trust via Machine Learning
###### Abstract  
Data trust in IoT is crucial for safeguarding privacy, security, reliable decision-making, user acceptance, and complying with regulations. Various approaches based on supervised or unsupervised machine learning (ML) have recently been proposed for evaluating IoT data trust. However, assessing their real-world efficacy is hard mainly due to the lack of related publicly available datasets that can be used for benchmarking. Since obtaining such datasets is challenging, we propose a data synthesis method, called random walk infilling (RWI), to augment IoT time-series datasets by synthesizing untrustworthy data from existing trustworthy data. Thus, RWI enables us to create labeled datasets that can be used to develop and validate ML models for IoT data trust evaluation. We also extract new features from IoT time-series sensor data that effectively capture its autocorrelation as well as its cross-correlation with the data of the neighboring (peer) sensors. These features can be used to learn ML models for recognizing the trustworthiness of IoT sensor data. Equipped with our synthesized ground-truth-labeled datasets and informative correlation-based features, we conduct extensive experiments to critically examine various approaches to evaluating IoT data trust via ML. The results reveal that commonly used ML-based approaches to IoT data trust evaluation, which rely on unsupervised cluster analysis to assign trust labels to unlabeled data, perform poorly. This poor performance is due to the underlying assumption that clustering provides reliable labels for data trust, which is found to be untenable. The results also indicate that ML models, when trained on datasets augmented via RWI and using the proposed features, generalize well to unseen data and surpass existing related approaches. Moreover, we observe that a semi-supervised ML approach that requires only about 10% of the data labeled offers competitive performance while being practically more appealing compared to the fully supervised approaches. The related Python code and data are available online. data synthesis; data trust; IoT sensor data; machine learning; semi-supervised learning; time-series data +
Footnote †: journal: _Future Internet_  
communication with other IoT devices or servers. The third layer is the application layer, which pertains to data processing and service delivery to the users [5]. In this paper, we address data trust at the perception layer where sensor data are susceptible to noise, error, or malicious manipulation [1; 5]. Previous research studies include several approaches to IoT data trust evaluation. For example, researchers have utilized blockchain to assess the trustworthiness of sensor data based on data correlation, sensor node reputation, and data confidence in an immutable manner [6]. Some researchers have proposed to evaluate IoT data trust by analyzing suspicious network behavior [7; 8; 9] or by examining raw sensor data [10; 11; 12]. However, most of these approaches evaluate the trustworthiness of sensor nodes [13; 14], rather than directly evaluating the trustworthiness of the sensor data. Evaluating data trustworthiness is crucial as IoT sensors may produce both trustworthy and untrustworthy data on different occasions or circumstances. Several existing works on evaluating IoT sensor data trust utilize supervised machine learning (ML) algorithms to classify the data in terms of trustworthiness in a binary fashion [10; 11; 12], i.e., trustworthy versus untrustworthy. To the best of our knowledge, there is no real-world IoT dataset that contains both trustworthy and untrustworthy data with ground-truth trust labels. Therefore, it is common to use clustering algorithms to artificially generate trust labels from unlabeled data. In addition, since raw time-series sensor data are usually high-dimensional, it is typical to represent the data using lower-dimensional features that are extracted from the raw data and facilitate the classification of its trustworthiness.