---
language:
- en
task_categories:
- other
task_ids:
- tabular-multi-class-classification
- multi-class-classification
pretty_name: Honeypot Cybersecurity Dataset
size_categories:
- 10K<n<100K
source_datasets:
- original
license: bsd-3-clause
tags:
- cybersecurity
- honeypot
- threat-intelligence
multilinguality:
- monolingual
annotations_creators:
- machine-generated
dataset_info:
  features:
  - name: dest_port
    dtype: int64
  - name: rdp
    dtype: float64
  - name: app_proto
    dtype: string
  - name: proto
    dtype: string
  - name: metadata
    dtype: string
  - name: tx_guessed
    dtype: bool
  - name: dest_ip
    dtype: string
  - name: '@version'
    dtype: int64
  - name: timestamp
    dtype: string
  - name: honeypot_ip_ext
    dtype: string
  - name: event_type
    dtype: string
  - name: pkt_src
    dtype: string
  - name: tx_id
    dtype: float64
  - name: geoip_ext
    dtype: string
  - name: geoip
    dtype: string
  - name: alert
    dtype: string
  - name: type
    dtype: string
  - name: in_iface
    dtype: string
  - name: '@timestamp'
    dtype: string
  - name: src_ip
    dtype: string
  - name: flow_id
    dtype: float64
  - name: src_port
    dtype: int64
  - name: direction
    dtype: string
  - name: host
    dtype: string
  - name: payload
    dtype: string
  - name: honeypot_hostname
    dtype: string
  - name: stream
    dtype: float64
  - name: payload_printable
    dtype: string
  - name: flow
    dtype: string
  - name: honeypot_ip_int
    dtype: string
  - name: tags
    dtype: float64
  - name: mitre_techniques
    dtype: string
  - name: attack_vectors
    dtype: string
  - name: mitre_tactic
    dtype: string
  - name: mitre_technique
    dtype: string
  - name: confidence_score
    dtype: float64
  - name: is_malicious
    dtype: bool
  - name: severity
    dtype: string
  - name: primary_label
    dtype: string
  - name: tcp
    dtype: string
  - name: subject
    dtype: string
  - name: uptime
    dtype: string
  - name: mod
    dtype: string
  - name: raw_freq
    dtype: string
  - name: raw_mtu
    dtype: float64
  - name: link
    dtype: string
  - name: params
    dtype: string
  - name: raw_sig
    dtype: string
  - name: dist
    dtype: float64
  - name: os
    dtype: string
  - name: ip_rep
    dtype: string
  - name: operation_mode
    dtype: float64
  - name: attack_connection
    dtype: string
  - name: end_time
    dtype: string
  - name: start_time
    dtype: string
  - name: download_tries
    dtype: float64
  - name: download_count
    dtype: float64
  - name: is_virtual
    dtype: bool
  - name: downloads
    dtype: string
  - name: proxy_connection
    dtype: string
  - name: reason
    dtype: string
  - name: raw_hits
    dtype: string
  - name: protocol
    dtype: string
  - name: action
    dtype: string
  - name: mstshash
    dtype: string
  - name: data
    dtype: string
  - name: fatt_rdp
    dtype: string
  - name: tls
    dtype: string
  - name: http
    dtype: float64
  - name: fileinfo
    dtype: float64
  - name: anomaly
    dtype: float64
  - name: files
    dtype: float64
  - name: fatt_tls
    dtype: float64
  - name: ssh
    dtype: float64
  - name: client
    dtype: float64
  - name: password
    dtype: float64
  - name: session
    dtype: float64
  - name: level
    dtype: float64
  - name: message
    dtype: float64
  - name: service
    dtype: float64
  - name: status
    dtype: float64
  - name: msg
    dtype: float64
  - name: username
    dtype: float64
  - name: fatt_ssh
    dtype: float64
  - name: lang
    dtype: float64
  - name: app
    dtype: float64
  - name: pgsql
    dtype: float64
  - name: input
    dtype: float64
  - name: output
    dtype: float64
  - name: environ
    dtype: float64
  - name: session_duration
    dtype: float64
  - name: smb
    dtype: float64
  - name: response.headers.Content-Type
    dtype: float64
  - name: response.metadata.provider
    dtype: float64
  - name: request.requestURI
    dtype: float64
  - name: request.method
    dtype: float64
  - name: request.headers.sortedSha256
    dtype: float64
  - name: response.body
    dtype: float64
  - name: response.metadata.temperature
    dtype: float64
  - name: response.headers.Server
    dtype: float64
  - name: request.body
    dtype: float64
  - name: request.headers.sorted
    dtype: float64
  - name: request.userAgent
    dtype: float64
  - name: hostname
    dtype: float64
  - name: response.metadata.generationSource
    dtype: float64
  - name: response.metadata.model
    dtype: float64
  - name: request.bodySha256
    dtype: float64
  - name: sensorName
    dtype: float64
  - name: request.protocol
    dtype: float64
  - name: fatt_http
    dtype: float64
  - name: commands
    dtype: float64
  splits:
  - name: train
    num_bytes: 189242
    num_examples: 100
  download_size: 65428
  dataset_size: 189242
config_name: default
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# network-intrusion-detection

## Dataset Description

This dataset contains cybersecurity events collected from honeypot infrastructure. 

The data has been processed and feature-engineered for machine learning applications in threat detection and security analytics.

## Feature Categories

### Network Features
- Connection flow statistics (bytes, packets, duration)
- Protocol-specific metrics
- Geographic information
- IP reputation data

### Behavioral Features
- Session patterns and command sequences
- User-agent analysis
- Attack pattern identification
- Protocol fingerprinting

### Temporal Features
- Time-based aggregations
- Frequency analysis
- Campaign detection indicators
- Attack timing patterns

### Security Labels
- MITRE ATT&CK technique mappings
- Alert severity classifications
- Automatic threat categorization
- Binary maliciousness indicators

## Usage Example

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("pyToshka/network-intrusion-detection")
train_data = dataset["train"]

# Basic exploration
print("Dataset features:", list(train_data.features.keys()))
print("Total samples:", len(train_data))

from collections import Counter

# Example: Filter RDP attacks
rdp_events = train_data.filter(lambda x: x['app_proto'] == 'rdp')
print("RDP events:", len(rdp_events))

# Example: Analyze attack vectors
if len(rdp_events) > 0:
    attack_vectors = Counter([event['attack_vectors'] for event in rdp_events if event['attack_vectors']])
    print("RDP Attack vectors:")
    for vector, count in attack_vectors.most_common():
        print(f"  {vector}: {count}")

# Example: Analyze protocol distribution
protocols = Counter([event['app_proto'] for event in train_data if event['app_proto']])
print("Protocol distribution:")
for proto, count in protocols.most_common():
    print(f"  {proto if proto else '(empty)'}: {count}")

# Example: Malicious events analysis
malicious_count = sum(1 for event in train_data if event['is_malicious'])
print(f"Malicious events: {malicious_count}/{len(train_data)} ({malicious_count/len(train_data)*100:.1f}%)")
```

## Data Fields

The dataset contains 77 features across several categories:

### Network Features
- `dest_ip`: Network-related information
- `src_ip`: Network-related information
- `dest_port`: Network-related information
- `geoip_ext`: Network-related information
- `honeypot_ip_int`: Network-related information
- ... and 7 more network features

### Behavioral Features
- `commands`: Behavioral analysis data

### Temporal Features
- `@timestamp`: Time-based information
- `timestamp`: Time-based information
- `uptime`: Time-based information

### Security Features
- `alert`: Security and threat intelligence
- `mitre_techniques`: Security and threat intelligence
- `attack_vectors`: Security and threat intelligence
- `mitre_tactic`: Security and threat intelligence
- `mitre_technique`: Security and threat intelligence
- ... and 1 more security features

## Data Splits

| Split | Examples |
|-------|----------|
| train | 29,545 |

## Dataset Statistics

- **Total size**: ~139.0 MB
- **Average record size**: ~4932 bytes
- **Feature completeness**: 100.0%

## Ethical Considerations

This dataset contains real honeypot data representing actual attack attempts. Users should:

- **Privacy**: Respect anonymization measures implemented in the dataset
- **Research Use**: Use data only for legitimate cybersecurity research and education
- **Responsible Disclosure**: Follow responsible disclosure practices for any findings
- **Legal Compliance**: Comply with applicable laws and regulations in your jurisdiction
- **No Reidentification**: Do not attempt to identify or contact attackers
- **Defensive Purpose**: Use insights for defensive security improvements only