Hmehdi515/NF-ToN-IoT-v2.csv

Dataset Card for NF-ToN-IoT Network Flow Dataset

Dataset Description

Dataset Summary

NF-ToN-IoT is a network flow dataset derived from IoT network traffic, containing both benign and attack flows. The dataset was created by converting pcap files from the ToN-IoT testbed into NetFlow records, providing labeled data for training network intrusion detection systems.

Size:

• Total flows: 11,858,887

• Benign samples: 4,270,402 (36.01%)

• Scanning samples: 2,646,685 (22.32%)

• XSS samples: 1,718,449 (14.49%)

• DDOS samples: 1,418,395 (11.96%)

• Password samples: 807,604 (6.81%)

• DOS samples: 498,905 (4.21%)

• Injection samples: 478,894 (4.04%)

• Backdoor samples: 11,824 (0.10%)

• MITM samples: 5,340 (0.04%)

• Ransomware samples: 2,389 (0.02%)

Data Fields

Field	Type	Description
IPV4_SRC_ADDR	string	Source IPv4 address
L4_SRC_PORT	integer	Source port number
IPV4_DST_ADDR	string	Destination IPv4 address
L4_DST_PORT	integer	Destination port number
PROTOCOL	integer	IP protocol identifier byte
L7_PROTO	integer	Layer 7 protocol
IN_BYTES	integer	Incoming bytes
OUT_BYTES	integer	Outgoing bytes
IN_PKTS	integer	Incoming packets
OUT_PKTS	integer	Outgoing packets
TCP_FLAGS	integer	TCP flags
FLOW_DURATION_MILLISECONDS	integer	Flow duration (ms)
Label	integer	Binary (0=benign, 1=attack)
Attack	string	Attack type or "Benign"

Attack Types

• DDoS/DoS
• Injection
• Scanning
• Password
• Ransomware
• XSS
• MITM
• Backdoor

Dataset Creation

Preprocessing

• Conversion from pcap to NetFlow records
• Feature extraction and normalization
• Label validation
• Balanced sampling (max 50,000 samples per class for training)

Uses

Intended Uses

• Training network intrusion detection systems
• Network anomaly detection
• Security analysis research
• Benchmarking security tools

Out-of-Scope Uses

• Direct production deployment
• Privacy-sensitive analysis
• Encrypted traffic analysis
• Development of attack tools

Considerations

Limitations

• Limited to specific IoT network configurations
• Controlled testbed environment
• May not represent all attack variants
• Temporal and geographic limitations

Ethical Considerations

• Should not be used for attack development
• Privacy considerations in network analysis
• Responsible vulnerability disclosure needed

Technical Details

Loading Code

import pandas as pd
from sklearn.preprocessing import MinMaxScaler

def load_dataset(path):
    df = pd.read_csv(path)
    numerical_features = [
        'L4_SRC_PORT', 'L4_DST_PORT', 'PROTOCOL', 'L7_PROTO',
        'IN_BYTES', 'OUT_BYTES', 'IN_PKTS', 'OUT_PKTS',
        'TCP_FLAGS', 'FLOW_DURATION_MILLISECONDS'
    ]
    df[numerical_features] = MinMaxScaler().fit_transform(df[numerical_features])
    return df

Distribution

• Format: CSV
• License: [License Information Needed]
• Citation: [Citation Information Needed]

Maintenance

Static dataset with possible future versions to include new attack patterns or IoT devices.