Context

Original dataset page, license, context, and description at link below: https://www.unb.ca/cic/datasets/darknet2020.html

This is a dataset gathered to test novel methods for classifying darknet traffic. Dataset gathered by the Canadian Institute for Cybersecurity at the University of New Brunswick.

Content

Each unique sample has a flow id. Additional columns include:

Src IP: Source IP Address Src Port: Source Port Dst IP: Destination IP Address Dst Port: Destination Port Protocol: Internet Protocol Version Timestamp: Timestamp for when traffic was sent Flow Duration: Duration Total Fwd Packet: Total number of packets from source to destination Total Bwd packets: Total Length of Fwd Packet Total Length of Bwd Packet Fwd Packet Length Max Fwd Packet Length Min Fwd Packet Length Mean Fwd Packet Length Std Bwd Packet Length Max Bwd Packet Length Min Bwd Packet Length Mean Bwd Packet Length Std Flow Bytes/s Flow Packets/s Flow IAT Mean Flow IAT Std Flow IAT Max Flow IAT Min Fwd IAT Total Fwd IAT Mean Fwd IAT Std Fwd IAT Max Fwd IAT Min Bwd IAT Total Bwd IAT Mean Bwd IAT Std Bwd IAT Max Bwd IAT Min Fwd PSH Flags Bwd PSH Flags Fwd URG Flags Bwd URG Flags Fwd Header Length Bwd Header Length Fwd Packets/s Bwd Packets/s Packet Length Min Packet Length Max Packet Length Mean Packet Length Std Packet Length Variance FIN Flag Count SYN Flag Count RST Flag Count PSH Flag Count ACK Flag Count URG Flag Count CWE Flag Count ECE Flag Count Down/Up Ratio Average Packet Size Fwd Segment Size Avg Bwd Segment Size Avg Fwd Bytes/Bulk Avg Fwd Packet/Bulk Avg Fwd Bulk Rate Avg Bwd Bytes/Bulk Avg Bwd Packet/Bulk Avg Bwd Bulk Rate Avg Subflow Fwd Packets Subflow Fwd Bytes Subflow Bwd Packets Subflow Bwd Bytes FWD Init Win Bytes Bwd Init Win Bytes Fwd Act Data Pkts Fwd Seg Size Min Active Mean Active Std Active Max Active Min Idle Mean Idle Std Idle Max Idle Min Label Label.1

Acknowledgements

• Canadian Institute for Cyber Security
• University of New Brunswick
• Kaggle

Original Paper: Arash Habibi Lashkari, Gurdip Kaur, and Abir Rahali, “DIDarknet: A Contemporary Approach to Detect and Characterize the Darknet Traffic using Deep Image Learning”, 10th International Conference on Communication and Network Security, Tokyo, Japan, November 2020

Inspiration

Wanting to better understand how Darknet routing works, and how to examine the traffic that goes through it.