Tracing Your Smart-Home Devices Conversations: A Real World IoT Traffic Data-Set

Area of Science:

• Cybersecurity
• Internet of Things (IoT)
• Network Security

Background:

• The rapid expansion of smart-home technology has introduced significant security vulnerabilities.
• Existing security solutions often lack user-friendliness and automation for IoT ecosystems.
• Protecting users' digital well-being in smart homes necessitates advanced security mechanisms.

Purpose of the Study:

• To present the Network Data Flow Analysis (NDFA) mechanism developed within the GHOST project.
• To demonstrate NDFA's capability for holistic monitoring of smart-home IoT network traffic.
• To introduce the GHOST-IoT-data-set, a novel dataset for smart-home security research.

Main Methods:

• Development of the NDFA traffic capturing and monitoring mechanism.
• Implementation of NDFA within the GHOST system architecture.
• Collection of the GHOST-IoT-data-set from a real-world smart-home installation across multiple network interfaces.

Main Results:

• The NDFA mechanism provides a holistic approach to monitoring smart-home IoT activity.
• The GHOST-IoT-data-set includes diverse traffic patterns, encompassing normal and simulated abnormal device behavior.
• The dataset was captured from a real-world smart-home setup, ensuring practical relevance.

Conclusions:

• NDFA is the first mechanism capable of comprehensively monitoring smart-home IoT network traffic.
• The GHOST-IoT-data-set serves as a proof of concept, showcasing NDFA's effectiveness in processing real-world IoT data.
• The research contributes a valuable resource for advancing smart-home security research and development.