Network Traffic Headers from 12 Containerized Applications During Specific Activities

12 containerized applications generated network traffic headers during their initial two minutes of execution. Mozhdeh Farhadi from the Centre National de la Recherche Scientifique collected and labeled each traffic sample with the application's engaged activity, such as downloading, browsing, or streaming. The dataset includes 60 samples from Curl, 47 from Firefox, and others from applications like Slack, Vlc, and wget.

Use Cases

• Train a classifier to identify application types based on network traffic headers.
• Analyze traffic patterns for specific activities like audio streaming or file downloading.
• Benchmark network-based anomaly detection models using labeled, containerized application traffic.

Strengths

• Traffic is labeled with the specific activity the application was performing, such as 'downloading' or 'audio streaming'.
• Data covers 12 distinct, isolated applications, providing a basis for comparative analysis.
• The collection window is standardized to the initial two minutes of application execution.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Mozhdeh Farhadi, Centre National de la Recherche Scientifique

Collection Method

Network traffic captured from containerized applications during specified activities.

Time Range

Initial two minutes of application execution.