Identifying Security and Privacy Violation Rules in Trigger-Action IoT Platforms with NLP Models

Trigger-Action platforms are systems that enable users to easily define, in terms of conditional rules, custom behaviors concerning Internet-of-Things (IoT) devices and web services. Unfortunately, although these tools stimulate the cre- ativity of users in building automation, they may also introduce serious risks for the users. Indeed, trigger-action rules can lead to the possibility of users harming themselves, for example by unintentionally disclosing non-public information, or unwillingly exposing their smart environment to cyber-threats. In this pa- per, we propose to use Natural Language Processing (NLP) techniques to detect automation rules, defined within Trigger- Action IoT platforms, that potentially violate the security or privacy of the users. The proposed NLP-based models capture the semantic and contextual information of the trigger-action rules by applying classification techniques to different combinations of rule’s features. We evaluate the proposed solution with the mainstream trigger-action platform, namely IFTTT, by training the NLP models with a dataset of 76,741 rules labeled by using an ensemble of three semi-supervised learning techniques. The experimental results demonstrate that the model based on BERT (Bidirectional Encoder Representations from Transformers) ob- tains the highest performances when trained on all features, achieving average Precision and Recall values between 88% and 93%. We also compare the achieved performances with those of a baseline system implementing information flow analysis