Burst Header Packet Flooding Attack Data for Optical Networks

UCI Machine Learning Repository hosts a dataset on Burst Header Packet (BHP) flooding attacks on Optical Burst Switching (OBS) networks. The dataset contains simulated attack data used for network security research. The original creator and update date are not specified.

Use Cases

• Classify network traffic as normal or attack using features like burst length and inter-arrival time.
• Train anomaly detection models on simulated BHP flooding attack patterns.
• Analyze the impact of attack parameters, such as burst header rate, on network performance metrics.
• Benchmark machine learning classifiers for detecting flooding attacks in optical switching environments.

Strengths

• Data is hosted on the UCI Machine Learning Repository, a known source for benchmark datasets.
• Dataset focuses on a specific, documented attack vector (BHP flooding) for a defined network architecture (OBS).

Limitations

• The dataset size, number of features, and specific column names are unknown.
• Data is simulated, which may not fully capture the complexity of real-world network traffic.

Provenance

Source

UCI Machine Learning Repository

Collection Method

Simulated network attack data