IoT Traffic Analyzer Tool with Automated and Holistic Feature Extraction Capability
The Internet of Things (IoT) is an emerging technology that attracted considerable attention in the last decade to become one of the most researched topics in computer science studies. This research aims to develop a benchmark framework for a public multi-task IoT traffic analyzer tool that holistic...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/11/5011 |
| _version_ | 1797596744327888896 |
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| author | Alanoud Subahi Miada Almasre |
| author_facet | Alanoud Subahi Miada Almasre |
| author_sort | Alanoud Subahi |
| collection | DOAJ |
| description | The Internet of Things (IoT) is an emerging technology that attracted considerable attention in the last decade to become one of the most researched topics in computer science studies. This research aims to develop a benchmark framework for a public multi-task IoT traffic analyzer tool that holistically extracts network traffic features from an IoT device in a smart home environment that researchers in various IoT industries can implement to collect information about IoT network behavior. A custom testbed with four IoT devices is created to collect real-time network traffic data based on seventeen comprehensive scenarios of these devices’ possible interactions. The output data is fed into the IoT traffic analyzer tool for both flow and packet levels analysis to extract all possible features. Such features are ultimately classified into five categories: IoT device type, IoT device behavior, Human interaction type, IoT behavior within the network, and Abnormal behavior. The tool is then evaluated by 20 users considering three variables: usefulness, accuracy of information being extracted, performance and usability. Users in three groups were highly satisfied with the interface and ease of use of the tool, with scores ranging from 90.5% to 93.8% and with an average score between 4.52 and 4.69 with a low standard deviation range, indicating that most of the data revolve around the mean |
| first_indexed | 2024-03-11T02:57:20Z |
| format | Article |
| id | doaj.art-18b1482ddd574d088cc7282e50ddc914 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T02:57:20Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-18b1482ddd574d088cc7282e50ddc9142023-11-18T08:31:19ZengMDPI AGSensors1424-82202023-05-012311501110.3390/s23115011IoT Traffic Analyzer Tool with Automated and Holistic Feature Extraction CapabilityAlanoud Subahi0Miada Almasre1Faculty of Computing and Information Technology, Department of Information Technology, King Abdulaziz University, Rabigh 25732, Saudi ArabiaFaculty of Computing and Information Technology, Department of Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaThe Internet of Things (IoT) is an emerging technology that attracted considerable attention in the last decade to become one of the most researched topics in computer science studies. This research aims to develop a benchmark framework for a public multi-task IoT traffic analyzer tool that holistically extracts network traffic features from an IoT device in a smart home environment that researchers in various IoT industries can implement to collect information about IoT network behavior. A custom testbed with four IoT devices is created to collect real-time network traffic data based on seventeen comprehensive scenarios of these devices’ possible interactions. The output data is fed into the IoT traffic analyzer tool for both flow and packet levels analysis to extract all possible features. Such features are ultimately classified into five categories: IoT device type, IoT device behavior, Human interaction type, IoT behavior within the network, and Abnormal behavior. The tool is then evaluated by 20 users considering three variables: usefulness, accuracy of information being extracted, performance and usability. Users in three groups were highly satisfied with the interface and ease of use of the tool, with scores ranging from 90.5% to 93.8% and with an average score between 4.52 and 4.69 with a low standard deviation range, indicating that most of the data revolve around the meanhttps://www.mdpi.com/1424-8220/23/11/5011IoT network trafficIoT traffic analysisIoT automatic feature extractionholistic traffic analysis |
| spellingShingle | Alanoud Subahi Miada Almasre IoT Traffic Analyzer Tool with Automated and Holistic Feature Extraction Capability Sensors IoT network traffic IoT traffic analysis IoT automatic feature extraction holistic traffic analysis |
| title | IoT Traffic Analyzer Tool with Automated and Holistic Feature Extraction Capability |
| title_full | IoT Traffic Analyzer Tool with Automated and Holistic Feature Extraction Capability |
| title_fullStr | IoT Traffic Analyzer Tool with Automated and Holistic Feature Extraction Capability |
| title_full_unstemmed | IoT Traffic Analyzer Tool with Automated and Holistic Feature Extraction Capability |
| title_short | IoT Traffic Analyzer Tool with Automated and Holistic Feature Extraction Capability |
| title_sort | iot traffic analyzer tool with automated and holistic feature extraction capability |
| topic | IoT network traffic IoT traffic analysis IoT automatic feature extraction holistic traffic analysis |
| url | https://www.mdpi.com/1424-8220/23/11/5011 |
| work_keys_str_mv | AT alanoudsubahi iottrafficanalyzertoolwithautomatedandholisticfeatureextractioncapability AT miadaalmasre iottrafficanalyzertoolwithautomatedandholisticfeatureextractioncapability |