Detecting IoT User Behavior and Sensitive Information in Encrypted IoT-App Traffic
Many people use smart-home devices, also known as the Internet of Things (IoT), in their daily lives. Most IoT devices come with a companion mobile application that users need to install on their smartphone or tablet to control, configure, and interface with the IoT device. IoT devices send informat...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/21/4777 |
| _version_ | 1798041962733895680 |
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| author | Alanoud Subahi George Theodorakopoulos |
| author_facet | Alanoud Subahi George Theodorakopoulos |
| author_sort | Alanoud Subahi |
| collection | DOAJ |
| description | Many people use smart-home devices, also known as the Internet of Things (IoT), in their daily lives. Most IoT devices come with a companion mobile application that users need to install on their smartphone or tablet to control, configure, and interface with the IoT device. IoT devices send information about their users from their app directly to the IoT manufacturer’s cloud; we call this the ”app-to-cloud way”. In this research, we invent a tool called IoT-app privacy inspector that can automatically infer the following from the IoT network traffic: the packet that reveals user interaction type with the IoT device via its app (e.g., login), the packets that carry sensitive Personal Identifiable Information (PII), the content type of such sensitive information (e.g., user’s location). We use Random Forest classifier as a supervised machine learning algorithm to extract features from network traffic. To train and test the three different multi-class classifiers, we collect and label network traffic from different IoT devices via their apps. We obtain the following classification accuracy values for the three aforementioned types of information: 99.4%, 99.8%, and 99.8%. This tool can help IoT users take an active role in protecting their privacy. |
| first_indexed | 2024-04-11T22:28:53Z |
| format | Article |
| id | doaj.art-f36bd17cfe5a40c292c9d588eb6672ac |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:28:53Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f36bd17cfe5a40c292c9d588eb6672ac2022-12-22T03:59:34ZengMDPI AGSensors1424-82202019-11-011921477710.3390/s19214777s19214777Detecting IoT User Behavior and Sensitive Information in Encrypted IoT-App TrafficAlanoud Subahi0George Theodorakopoulos1School of Computer Science and Informatics, Cardiff University, Cardiff CF10 3AT, UKSchool of Computer Science and Informatics, Cardiff University, Cardiff CF10 3AT, UKMany people use smart-home devices, also known as the Internet of Things (IoT), in their daily lives. Most IoT devices come with a companion mobile application that users need to install on their smartphone or tablet to control, configure, and interface with the IoT device. IoT devices send information about their users from their app directly to the IoT manufacturer’s cloud; we call this the ”app-to-cloud way”. In this research, we invent a tool called IoT-app privacy inspector that can automatically infer the following from the IoT network traffic: the packet that reveals user interaction type with the IoT device via its app (e.g., login), the packets that carry sensitive Personal Identifiable Information (PII), the content type of such sensitive information (e.g., user’s location). We use Random Forest classifier as a supervised machine learning algorithm to extract features from network traffic. To train and test the three different multi-class classifiers, we collect and label network traffic from different IoT devices via their apps. We obtain the following classification accuracy values for the three aforementioned types of information: 99.4%, 99.8%, and 99.8%. This tool can help IoT users take an active role in protecting their privacy.https://www.mdpi.com/1424-8220/19/21/4777iotprivacysupervised machine learningiot privacy inspector |
| spellingShingle | Alanoud Subahi George Theodorakopoulos Detecting IoT User Behavior and Sensitive Information in Encrypted IoT-App Traffic Sensors iot privacy supervised machine learning iot privacy inspector |
| title | Detecting IoT User Behavior and Sensitive Information in Encrypted IoT-App Traffic |
| title_full | Detecting IoT User Behavior and Sensitive Information in Encrypted IoT-App Traffic |
| title_fullStr | Detecting IoT User Behavior and Sensitive Information in Encrypted IoT-App Traffic |
| title_full_unstemmed | Detecting IoT User Behavior and Sensitive Information in Encrypted IoT-App Traffic |
| title_short | Detecting IoT User Behavior and Sensitive Information in Encrypted IoT-App Traffic |
| title_sort | detecting iot user behavior and sensitive information in encrypted iot app traffic |
| topic | iot privacy supervised machine learning iot privacy inspector |
| url | https://www.mdpi.com/1424-8220/19/21/4777 |
| work_keys_str_mv | AT alanoudsubahi detectingiotuserbehaviorandsensitiveinformationinencryptediotapptraffic AT georgetheodorakopoulos detectingiotuserbehaviorandsensitiveinformationinencryptediotapptraffic |