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--- |
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language: |
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- en |
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task_categories: |
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- other |
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task_ids: |
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- tabular-multi-class-classification |
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- multi-class-classification |
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pretty_name: Honeypot Cybersecurity Dataset |
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size_categories: |
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- 10K<n<100K |
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source_datasets: |
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- original |
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license: bsd-3-clause |
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tags: |
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- cybersecurity |
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- honeypot |
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- threat-intelligence |
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multilinguality: |
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- monolingual |
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annotations_creators: |
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- machine-generated |
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dataset_info: |
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features: |
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- name: dest_port |
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dtype: int64 |
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- name: rdp |
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dtype: float64 |
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- name: app_proto |
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dtype: string |
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- name: proto |
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dtype: string |
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- name: metadata |
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dtype: string |
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- name: tx_guessed |
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dtype: bool |
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- name: dest_ip |
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dtype: string |
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- name: '@version' |
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dtype: int64 |
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- name: timestamp |
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dtype: string |
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- name: honeypot_ip_ext |
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dtype: string |
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- name: event_type |
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dtype: string |
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- name: pkt_src |
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dtype: string |
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- name: tx_id |
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dtype: float64 |
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- name: geoip_ext |
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dtype: string |
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- name: geoip |
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dtype: string |
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- name: alert |
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dtype: string |
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- name: type |
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dtype: string |
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- name: in_iface |
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dtype: string |
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- name: '@timestamp' |
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dtype: string |
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- name: src_ip |
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dtype: string |
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- name: flow_id |
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dtype: float64 |
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- name: src_port |
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dtype: int64 |
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- name: direction |
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dtype: string |
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- name: host |
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dtype: string |
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- name: payload |
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dtype: string |
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- name: honeypot_hostname |
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dtype: string |
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- name: stream |
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dtype: float64 |
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- name: payload_printable |
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dtype: string |
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- name: flow |
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dtype: string |
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- name: honeypot_ip_int |
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dtype: string |
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- name: tags |
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dtype: float64 |
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- name: mitre_techniques |
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dtype: string |
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- name: attack_vectors |
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dtype: string |
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- name: mitre_tactic |
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dtype: string |
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- name: mitre_technique |
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dtype: string |
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- name: confidence_score |
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dtype: float64 |
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- name: is_malicious |
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dtype: bool |
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- name: severity |
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dtype: string |
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- name: primary_label |
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dtype: string |
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- name: tcp |
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dtype: string |
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- name: subject |
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dtype: string |
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- name: uptime |
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dtype: string |
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- name: mod |
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dtype: string |
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- name: raw_freq |
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dtype: string |
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- name: raw_mtu |
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dtype: float64 |
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- name: link |
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dtype: string |
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- name: params |
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dtype: string |
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- name: raw_sig |
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dtype: string |
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- name: dist |
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dtype: float64 |
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- name: os |
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dtype: string |
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- name: ip_rep |
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dtype: string |
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- name: operation_mode |
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dtype: float64 |
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- name: attack_connection |
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dtype: string |
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- name: end_time |
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dtype: string |
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- name: start_time |
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dtype: string |
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- name: download_tries |
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dtype: float64 |
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- name: download_count |
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dtype: float64 |
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- name: is_virtual |
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dtype: bool |
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- name: downloads |
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dtype: string |
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- name: proxy_connection |
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dtype: string |
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- name: reason |
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dtype: string |
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- name: raw_hits |
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dtype: string |
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- name: protocol |
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dtype: string |
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- name: action |
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dtype: string |
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- name: mstshash |
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dtype: string |
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- name: data |
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dtype: string |
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- name: fatt_rdp |
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dtype: string |
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- name: tls |
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dtype: string |
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- name: http |
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dtype: float64 |
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- name: fileinfo |
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dtype: float64 |
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- name: anomaly |
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dtype: float64 |
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- name: files |
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dtype: float64 |
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- name: fatt_tls |
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dtype: float64 |
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- name: ssh |
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dtype: float64 |
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- name: client |
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dtype: float64 |
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- name: password |
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dtype: float64 |
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- name: session |
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dtype: float64 |
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- name: level |
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dtype: float64 |
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- name: message |
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dtype: float64 |
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- name: service |
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dtype: float64 |
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- name: status |
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dtype: float64 |
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- name: msg |
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dtype: float64 |
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- name: username |
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dtype: float64 |
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- name: fatt_ssh |
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dtype: float64 |
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- name: lang |
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dtype: float64 |
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- name: app |
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dtype: float64 |
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- name: pgsql |
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dtype: float64 |
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- name: input |
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dtype: float64 |
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- name: output |
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dtype: float64 |
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- name: environ |
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dtype: float64 |
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- name: session_duration |
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dtype: float64 |
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- name: smb |
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dtype: float64 |
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- name: response.headers.Content-Type |
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dtype: float64 |
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- name: response.metadata.provider |
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dtype: float64 |
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- name: request.requestURI |
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dtype: float64 |
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- name: request.method |
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dtype: float64 |
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- name: request.headers.sortedSha256 |
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dtype: float64 |
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- name: response.body |
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dtype: float64 |
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- name: response.metadata.temperature |
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dtype: float64 |
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- name: response.headers.Server |
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dtype: float64 |
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- name: request.body |
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dtype: float64 |
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- name: request.headers.sorted |
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dtype: float64 |
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- name: request.userAgent |
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dtype: float64 |
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- name: hostname |
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dtype: float64 |
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- name: response.metadata.generationSource |
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dtype: float64 |
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- name: response.metadata.model |
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dtype: float64 |
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- name: request.bodySha256 |
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dtype: float64 |
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- name: sensorName |
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dtype: float64 |
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- name: request.protocol |
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dtype: float64 |
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- name: fatt_http |
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dtype: float64 |
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- name: commands |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 189242 |
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num_examples: 100 |
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download_size: 65428 |
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dataset_size: 189242 |
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config_name: default |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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# network-intrusion-detection |
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## Dataset Description |
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This dataset contains cybersecurity events collected from honeypot infrastructure. |
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The data has been processed and feature-engineered for machine learning applications in threat detection and security analytics. |
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## Feature Categories |
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### Network Features |
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- Connection flow statistics (bytes, packets, duration) |
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- Protocol-specific metrics |
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- Geographic information |
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- IP reputation data |
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### Behavioral Features |
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- Session patterns and command sequences |
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- User-agent analysis |
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- Attack pattern identification |
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- Protocol fingerprinting |
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### Temporal Features |
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- Time-based aggregations |
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- Frequency analysis |
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- Campaign detection indicators |
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- Attack timing patterns |
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### Security Labels |
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- MITRE ATT&CK technique mappings |
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- Alert severity classifications |
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- Automatic threat categorization |
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- Binary maliciousness indicators |
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## Usage Example |
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```python |
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from datasets import load_dataset |
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# Load the dataset |
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dataset = load_dataset("pyToshka/network-intrusion-detection") |
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train_data = dataset["train"] |
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# Basic exploration |
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print("Dataset features:", list(train_data.features.keys())) |
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print("Total samples:", len(train_data)) |
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from collections import Counter |
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# Example: Filter RDP attacks |
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rdp_events = train_data.filter(lambda x: x['app_proto'] == 'rdp') |
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print("RDP events:", len(rdp_events)) |
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# Example: Analyze attack vectors |
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if len(rdp_events) > 0: |
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attack_vectors = Counter([event['attack_vectors'] for event in rdp_events if event['attack_vectors']]) |
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print("RDP Attack vectors:") |
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for vector, count in attack_vectors.most_common(): |
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print(f" {vector}: {count}") |
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# Example: Analyze protocol distribution |
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protocols = Counter([event['app_proto'] for event in train_data if event['app_proto']]) |
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print("Protocol distribution:") |
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for proto, count in protocols.most_common(): |
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print(f" {proto if proto else '(empty)'}: {count}") |
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# Example: Malicious events analysis |
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malicious_count = sum(1 for event in train_data if event['is_malicious']) |
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print(f"Malicious events: {malicious_count}/{len(train_data)} ({malicious_count/len(train_data)*100:.1f}%)") |
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``` |
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## Data Fields |
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The dataset contains 77 features across several categories: |
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### Network Features |
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- `dest_ip`: Network-related information |
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- `src_ip`: Network-related information |
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- `dest_port`: Network-related information |
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- `geoip_ext`: Network-related information |
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- `honeypot_ip_int`: Network-related information |
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- ... and 7 more network features |
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### Behavioral Features |
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- `commands`: Behavioral analysis data |
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### Temporal Features |
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- `@timestamp`: Time-based information |
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- `timestamp`: Time-based information |
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- `uptime`: Time-based information |
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### Security Features |
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- `alert`: Security and threat intelligence |
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- `mitre_techniques`: Security and threat intelligence |
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- `attack_vectors`: Security and threat intelligence |
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- `mitre_tactic`: Security and threat intelligence |
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- `mitre_technique`: Security and threat intelligence |
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- ... and 1 more security features |
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## Data Splits |
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| Split | Examples | |
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|-------|----------| |
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| train | 29,545 | |
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## Dataset Statistics |
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- **Total size**: ~139.0 MB |
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- **Average record size**: ~4932 bytes |
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- **Feature completeness**: 100.0% |
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## Ethical Considerations |
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This dataset contains real honeypot data representing actual attack attempts. Users should: |
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- **Privacy**: Respect anonymization measures implemented in the dataset |
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- **Research Use**: Use data only for legitimate cybersecurity research and education |
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- **Responsible Disclosure**: Follow responsible disclosure practices for any findings |
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- **Legal Compliance**: Comply with applicable laws and regulations in your jurisdiction |
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- **No Reidentification**: Do not attempt to identify or contact attackers |
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- **Defensive Purpose**: Use insights for defensive security improvements only |
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