TY - GEN
T1 - ProvPredictor
T2 - 2nd EAI International Conference on Security and Privacy in Cyber-Physical Systems and Smart Vehicles, SmartSP 2024
AU - Norris, Michael
AU - McDaniel, Patrick
AU - Rafiul Hussain, Syed
AU - Tan, Gang
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.
PY - 2025
Y1 - 2025
N2 - Internet of Things (IoT) platforms possess several properties that can potentially jeopardize the safety and security of users, such as distributed deployment, processing sensitive information, and exposed networks. This issue is further exacerbated by the physical nature of IoT allowing devices to compromise the confidentiality and integrity of both persons and property.Some IoT problems can be addressed by taking preventative measures before they happen, which requires making predictions about the future. We design ProvPredictor to gather provenance information in order to train a model to make predictions about potentially unsafe behaviors in the future. To demonstrate the effectiveness of ProvPredictor, we create a realistic deployment using IFTTT, a web-based IoT platform, using the most common IFTTT compatible services and applications in a home environment. We additionally use Agriculture datasets to show how ProvPredictor can operate in industrial systems. We train ProvPredictor on the generated provenance data and find that ProvPredictor can predict violations with over 90% accuracy. With ProvPredictor we demonstrate the advantage that provenance information provides to IoT and the feasibility of a provenance collector that focuses on predicting future behavior.
AB - Internet of Things (IoT) platforms possess several properties that can potentially jeopardize the safety and security of users, such as distributed deployment, processing sensitive information, and exposed networks. This issue is further exacerbated by the physical nature of IoT allowing devices to compromise the confidentiality and integrity of both persons and property.Some IoT problems can be addressed by taking preventative measures before they happen, which requires making predictions about the future. We design ProvPredictor to gather provenance information in order to train a model to make predictions about potentially unsafe behaviors in the future. To demonstrate the effectiveness of ProvPredictor, we create a realistic deployment using IFTTT, a web-based IoT platform, using the most common IFTTT compatible services and applications in a home environment. We additionally use Agriculture datasets to show how ProvPredictor can operate in industrial systems. We train ProvPredictor on the generated provenance data and find that ProvPredictor can predict violations with over 90% accuracy. With ProvPredictor we demonstrate the advantage that provenance information provides to IoT and the feasibility of a provenance collector that focuses on predicting future behavior.
UR - https://www.scopus.com/pages/publications/105010144031
UR - https://www.scopus.com/pages/publications/105010144031#tab=citedBy
U2 - 10.1007/978-3-031-93354-7_6
DO - 10.1007/978-3-031-93354-7_6
M3 - Conference contribution
AN - SCOPUS:105010144031
SN - 9783031933530
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 110
EP - 134
BT - Security and Privacy in Cyber-Physical Systems and Smart Vehicles - 2nd EAI International Conference, SmartSP 2024, Proceedings
A2 - Hei, Xiali
A2 - Garcia, Luis
A2 - Kim, Taegyu
A2 - Kim, Kyungtae
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 7 November 2024 through 8 November 2024
ER -