The Graph-Based Dataset for IoT Network Attack Detection is a curated collection of data specifically designed for research and development in the field of cybersecurity, focusing on the detection of attacks in Internet of Things (IoT) networks. This graph-based dataset provides researchers, developers, and practitioners with a comprehensive resource to evaluate and benchmark various detection algorithms and systems in real-world IoT network environments.
## Dataset Description
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The dataset consists of network traffic data captured from emulated IoT network environments, where various attack scenarios have been emulated. The network traffic data is represented in the form of graphs, capturing the interactions and relationships between different devices, services, and communication patterns within the IoT network. Each graph in the dataset represents a snapshot of network activity over a specific time period, enabling analysis of attack patterns and behaviors. The following figure presents the general netwok architecture.
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You find in this dataset the graph that contains all the attacks and also one graph per attack. The graph containing all attacks is the merge of attack graphs include normal traffic.
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Conversely, the adversary network features a botnet comprising multiple zombie machines overseen by a singular bot-master machine functioning as a Command and Control (C&C) server. Through this central machine, we can orchestrate an array of botnet attacks directed at the user network. The following figure illustrates the architectural of our testbed.
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For implementing this environment, we used the GNS3 tool. It is an open-source software for network emulation. It empowers users to design, configure, and test intricate network topologies within a virtual environment.
