Network Traffic Attributes for Botmaster Detection in Multi-Cloud Environments

Network traffic data designed to identify botmasters in multi-cloud environments. The dataset was created by Gautam M. Borkar and uses an ensemble-learning mechanism based on random forest algorithms. The data includes attributes such as access time, destination IP address, port number, and protocol type.

Use Cases

• Classifying malicious network traffic based on attributes like access time and protocol type
• Clustering network traffic patterns using Web Access Pattern-tree (WAP) based structural mining
• Training ensemble models for botmaster identification using decrypted network traffic features

Strengths

• Describes a multi-stage analytical pipeline for botnet attack detection
• Includes specific attribute types for classification: Access time, Destination IP address, port number, and protocol type

Limitations

• Row count and file size are unknown, limiting suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download
• Last update date is unknown; freshness unverified

Provenance

Source

Gautam M. Borkar

Collection Method

Proposed via agglomerative-divisive based web usage mining and web structural mining on network traffic.