The Internet of Things (IoT) has been erupting the world widely over the decade. Smart home and smart building owners are increasingly deploying IoT devices to monitor and control their environments due to the rapid decline in the price of IoT devices. The recent intensive research has shown that network traffic traces of IoT devices have significant cybersecurity and privacy issues. These security and privacy defending techniques have enabled sophisticated approaches to ensure security and preserve user privacy. However, due to the fact that different approaches are evaluated using their own datasets, their own developed security and privacy attack models, and their own evaluating metrics, it is being significantly difficult to make a fair and comprehensive comparisons among different IoT security strengthening and user privacy preserving research to better understand IoT security issues and end-user benefits. To address this problem, we present a deep learning-based adversarial attack model framework-SmartAttack, which enables a set of sophisticated adversarial attack models that can be leveraged by researchers and industrial users from IoT security community to better evaluate their work. In essence, we leverage the most widely used unsupervised machine learning and deep learning models to design and implement these attack models. SmartAttack also provides user options to select the detailed configuration for each attack model, such as kernel, dataset splitting, cross-validation states, and evaluating metrics. We also evaluate the performance of SmartAttack using two different datasets. In addition, we made the source codes and the related datasets of SmartAttack publicly-available on our research website such that researchers can use our SmartAttack to benchmark their security strengthening and privacy-preserving approaches.
