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KR-Mobile-Smishing-SIEM-Dataset-2026

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Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
incident_id: string
case_type: string
theme: string
difficulty_tier: string
timeline_profile: string
start_time: timestamp[s]
end_time: timestamp[s]
platform_coverage: list<item: string>
minimum_log_sources_required: list<item: string>
full_log_sources_used: list<item: string>
noise_model_applied: struct<profile: string, background_event_ratio: double>
message_samples_used: list<item: struct<target_device: string, text: string, sender_type: string, delivery_channel: string>>
page_construction_detail_safe: struct<no_brand_copy: bool, stepper_ui_flow: list<item: string>, visual_style_notes: list<item: string>, form_risk_class: string, fields_requested: list<item: string>, detection_cues_for_human_analyst: list<item: string>>
persona: struct<user_id: string, job_title: string, risk_profile: string, devices: list<item: struct<device_id: string, platform: string, os_version: string>>>
behavioral_model_for_this_incident: struct<scenario: string, why_this_behavior: string, time_gap_rationale: string>
expected_rules: struct<must_fire: list<item: string>, should_fire: list<item: string>, optional: list<item: null>, expected_fire_path: struct<SMISH_REDIRECT_CHAIN: struct<platform: string, trigger_events: list<item: string>>, SENSITIVE_FORM_SEQUENCE: struct<platform: string, trigger_events: list<item: string>>, POST_LURE_FINANCIAL_RISK_SIGNAL: struct<platform: string, trigger_events: list<item: string>>>>
event_stream: list<item: struct<_event_index: string, _phase: string, event_id: string, time: timestamp[s], log_source: string, event_type: string, severity: string, entity: struct<user_id: string, device_id: string, platform: string, os_version: string, mdm_enrolled: bool, device_fingerprint_status: string>, network: struct<carrier: string, carrier_network_type: string, roaming: bool, dst_domain: string, dst_ip: string, ttl_sec: int64, domain_age_days: int64, domain_reputation: double, resolver: string, http: struct<method: string, path: string, status_code: int64>, src_ip: string>, object: struct<sender_type: string, sender_id: string, contains_url: bool, message_features: struct<urgency_language: bool, sender_verification: bool>, qtype: string, traffic_category: string, redirect_hops_total: int64, referrer_chain_domains_masked: list<item: string>, behavior: struct<signal: string, user_interaction: string, dwell_time_ms: int64, form_focus_detected: bool, tap_next: string>, user_initiated: bool, auth: struct<method: string, challenge_id: string, result: string, reason: string, burst_counter_count: int64>, risk_decision: string, result: string, fraud_score: double, flag_reason: string>, correlation: struct<incident_id: string, message_id: string, session_id: string, redirect_chain_id: string, bank_corr_id: string>, label: struct<ground_truth: string, case_type: string, attack_stage: string, mitre_mobile: string, theme: string>, original_source_hint: string, noise_metadata: struct<duplicate_of: string, reason: string>>>
event_stream_timeline_summary: struct<total_events: int64, malicious_events: int64, benign_context_events: int64, noise_events_included: int64, phases: list<item: struct<phase: string, time_range: string, events: int64, desc: string>>, total_elapsed_minutes: double, key_time_gaps_explained: struct<Initial_Response: string, Session_Gap: string, Impact_Delay: string>>
rule_fire_verification: struct<SMISH_REDIRECT_CHAIN: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>, PHISH_WARNING_BYPASS: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>, SENSITIVE_FORM_SEQUENCE: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>, PLATFORM_DIVERGENCE: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>, POST_LURE_FINANCIAL_RISK_SIGNAL: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>>
scaling_template: struct<to_reach_200_events: struct<background_events_to_add: int64, background_composition: struct<messaging_sync: int64, email_sync: int64, push_notification: int64, software_update: int64, cdn_traffic: int64, utility_widgets: int64, mdm_checkin: int64, cloud_backup: int64>>>
vs
incident_id: string
case_type: string
theme: string
difficulty_tier: string
timeline_profile: string
start_time: timestamp[s]
end_time: timestamp[s]
platform_coverage: list<item: string>
minimum_log_sources_required: list<item: string>
full_log_sources_used: list<item: string>
noise_model_applied: struct<profile: string, background_event_ratio: double>
message_samples_used: list<item: struct<target_device: string, text: string, sender_type: string, delivery_channel: string>>
page_construction_detail_safe: struct<no_brand_copy: bool, stepper_ui_flow: list<item: string>, visual_style_notes: list<item: string>, form_risk_class: string, fields_requested: list<item: null>, detection_cues_for_human_analyst: list<item: null>>
persona: struct<user_id: string, job_title: string, risk_profile: string, devices: list<item: struct<device_id: string, platform: string, os_version: string>>>
behavioral_model_for_this_incident: struct<scenario: string, why_this_behavior: string, time_gap_rationale: string>
expected_rules: struct<must_fire: list<item: null>, should_fire: list<item: null>, optional: list<item: null>, expected_fire_path: struct<>>
event_stream: list<item: struct<_event_index: string, _phase: string, event_id: string, time: timestamp[s], log_source: string, event_type: string, severity: string, entity: struct<user_id: string, device_id: string, platform: string, os_version: string, mdm_enrolled: bool, device_fingerprint_status: string, browser: string>, network: struct<carrier: string, carrier_network_type: string, roaming: bool, dst_domain: string, dst_ip: string, ttl_sec: int64, domain_age_days: int64, domain_reputation: double, resolver: string, sni: string, tls_version: string, http: struct<method: string, path: string, status_code: int64, content_type: string, latency_ms: int64>>, object: struct<sender_type: string, sender_id: string, contains_url: bool, message_features: struct<urgency_language: bool, sender_verification: bool>, traffic_category: string, qtype: string, user_initiated: bool, posture: string, secure_dns_or_vpn: bool, os_patch_level: timestamp[s]>, correlation: struct<incident_id: string, message_id: string, session_id: string>, label: struct<ground_truth: string, case_type: string, attack_stage: null, activity_stage: string, theme: string>, original_source_hint: string, noise_metadata: struct<duplicate_of: string, reason: string>>>
event_stream_timeline_summary: struct<total_events: int64, malicious_events: int64, benign_context_events: int64, noise_events_included: int64, phases: list<item: struct<phase: string, time_range: string, events: int64, desc: string>>, total_elapsed_minutes: double, key_time_gaps_explained: struct<sms_to_click: string, page_visit_to_end: string>>
rule_fire_verification: struct<SMISH_REDIRECT_CHAIN: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>, PHISH_WARNING_BYPASS: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>, SENSITIVE_FORM_SEQUENCE: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>, PLATFORM_DIVERGENCE: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>, POST_LURE_FINANCIAL_RISK_SIGNAL: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>>
scaling_template: struct<to_reach_200_events: struct<background_events_to_add: int64, background_composition: struct<messaging_sync: int64, email_sync: int64, push_notification: int64, software_update: int64, cdn_traffic: int64, utility_widgets: int64, mdm_checkin: int64, cloud_backup: int64>>>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3608, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2368, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2082, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 604, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              incident_id: string
              case_type: string
              theme: string
              difficulty_tier: string
              timeline_profile: string
              start_time: timestamp[s]
              end_time: timestamp[s]
              platform_coverage: list<item: string>
              minimum_log_sources_required: list<item: string>
              full_log_sources_used: list<item: string>
              noise_model_applied: struct<profile: string, background_event_ratio: double>
              message_samples_used: list<item: struct<target_device: string, text: string, sender_type: string, delivery_channel: string>>
              page_construction_detail_safe: struct<no_brand_copy: bool, stepper_ui_flow: list<item: string>, visual_style_notes: list<item: string>, form_risk_class: string, fields_requested: list<item: string>, detection_cues_for_human_analyst: list<item: string>>
              persona: struct<user_id: string, job_title: string, risk_profile: string, devices: list<item: struct<device_id: string, platform: string, os_version: string>>>
              behavioral_model_for_this_incident: struct<scenario: string, why_this_behavior: string, time_gap_rationale: string>
              expected_rules: struct<must_fire: list<item: string>, should_fire: list<item: string>, optional: list<item: null>, expected_fire_path: struct<SMISH_REDIRECT_CHAIN: struct<platform: string, trigger_events: list<item: string>>, SENSITIVE_FORM_SEQUENCE: struct<platform: string, trigger_events: list<item: string>>, POST_LURE_FINANCIAL_RISK_SIGNAL: struct<platform: string, trigger_events: list<item: string>>>>
              event_stream: list<item: struct<_event_index: string, _phase: string, event_id: string, time: timestamp[s], log_source: string, event_type: string, severity: string, entity: struct<user_id: string, device_id: string, platform: string, os_version: string, mdm_enrolled: bool, device_fingerprint_status: string>, network: struct<carrier: string, carrier_network_type: string, roaming: bool, dst_domain: string, dst_ip: string, ttl_sec: int64, domain_age_days: int64, domain_reputation: double, resolver: string, http: struct<method: string, path: string, status_code: int64>, src_ip: string>, object: struct<sender_type: string, sender_id: string, contains_url: bool, message_features: struct<urgency_language: bool, sender_verification: bool>, qtype: string, traffic_category: string, redirect_hops_total: int64, referrer_chain_domains_masked: list<item: string>, behavior: struct<signal: string, user_interaction: string, dwell_time_ms: int64, form_focus_detected: bool, tap_next: string>, user_initiated: bool, auth: struct<method: string, challenge_id: string, result: string, reason: string, burst_counter_count: int64>, risk_decision: string, result: string, fraud_score: double, flag_reason: string>, correlation: struct<incident_id: string, message_id: string, session_id: string, redirect_chain_id: string, bank_corr_id: string>, label: struct<ground_truth: string, case_type: string, attack_stage: string, mitre_mobile: string, theme: string>, original_source_hint: string, noise_metadata: struct<duplicate_of: string, reason: string>>>
              event_stream_timeline_summary: struct<total_events: int64, malicious_events: int64, benign_context_events: int64, noise_events_included: int64, phases: list<item: struct<phase: string, time_range: string, events: int64, desc: string>>, total_elapsed_minutes: double, key_time_gaps_explained: struct<Initial_Response: string, Session_Gap: string, Impact_Delay: string>>
              rule_fire_verification: struct<SMISH_REDIRECT_CHAIN: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>, PHISH_WARNING_BYPASS: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>, SENSITIVE_FORM_SEQUENCE: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>, PLATFORM_DIVERGENCE: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>, POST_LURE_FINANCIAL_RISK_SIGNAL: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>>
              scaling_template: struct<to_reach_200_events: struct<background_events_to_add: int64, background_composition: struct<messaging_sync: int64, email_sync: int64, push_notification: int64, software_update: int64, cdn_traffic: int64, utility_widgets: int64, mdm_checkin: int64, cloud_backup: int64>>>
              vs
              incident_id: string
              case_type: string
              theme: string
              difficulty_tier: string
              timeline_profile: string
              start_time: timestamp[s]
              end_time: timestamp[s]
              platform_coverage: list<item: string>
              minimum_log_sources_required: list<item: string>
              full_log_sources_used: list<item: string>
              noise_model_applied: struct<profile: string, background_event_ratio: double>
              message_samples_used: list<item: struct<target_device: string, text: string, sender_type: string, delivery_channel: string>>
              page_construction_detail_safe: struct<no_brand_copy: bool, stepper_ui_flow: list<item: string>, visual_style_notes: list<item: string>, form_risk_class: string, fields_requested: list<item: null>, detection_cues_for_human_analyst: list<item: null>>
              persona: struct<user_id: string, job_title: string, risk_profile: string, devices: list<item: struct<device_id: string, platform: string, os_version: string>>>
              behavioral_model_for_this_incident: struct<scenario: string, why_this_behavior: string, time_gap_rationale: string>
              expected_rules: struct<must_fire: list<item: null>, should_fire: list<item: null>, optional: list<item: null>, expected_fire_path: struct<>>
              event_stream: list<item: struct<_event_index: string, _phase: string, event_id: string, time: timestamp[s], log_source: string, event_type: string, severity: string, entity: struct<user_id: string, device_id: string, platform: string, os_version: string, mdm_enrolled: bool, device_fingerprint_status: string, browser: string>, network: struct<carrier: string, carrier_network_type: string, roaming: bool, dst_domain: string, dst_ip: string, ttl_sec: int64, domain_age_days: int64, domain_reputation: double, resolver: string, sni: string, tls_version: string, http: struct<method: string, path: string, status_code: int64, content_type: string, latency_ms: int64>>, object: struct<sender_type: string, sender_id: string, contains_url: bool, message_features: struct<urgency_language: bool, sender_verification: bool>, traffic_category: string, qtype: string, user_initiated: bool, posture: string, secure_dns_or_vpn: bool, os_patch_level: timestamp[s]>, correlation: struct<incident_id: string, message_id: string, session_id: string>, label: struct<ground_truth: string, case_type: string, attack_stage: null, activity_stage: string, theme: string>, original_source_hint: string, noise_metadata: struct<duplicate_of: string, reason: string>>>
              event_stream_timeline_summary: struct<total_events: int64, malicious_events: int64, benign_context_events: int64, noise_events_included: int64, phases: list<item: struct<phase: string, time_range: string, events: int64, desc: string>>, total_elapsed_minutes: double, key_time_gaps_explained: struct<sms_to_click: string, page_visit_to_end: string>>
              rule_fire_verification: struct<SMISH_REDIRECT_CHAIN: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>, PHISH_WARNING_BYPASS: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>, SENSITIVE_FORM_SEQUENCE: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>, PLATFORM_DIVERGENCE: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>, POST_LURE_FINANCIAL_RISK_SIGNAL: struct<fires: bool, fire_path: string, trigger: string, within_window: bool>>
              scaling_template: struct<to_reach_200_events: struct<background_events_to_add: int64, background_composition: struct<messaging_sync: int64, email_sync: int64, push_notification: int64, software_update: int64, cdn_traffic: int64, utility_widgets: int64, mdm_checkin: int64, cloud_backup: int64>>>

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license: cc-by-nc-nd-4.0 task_categories:

  • text-classification language:
  • ko tags:
  • cybersecurity
  • smishing
  • phishing
  • siem
  • mobile-security
  • synthetic-data
  • korean
  • mitre-attack
  • threat-detection
  • fraud-detection pretty_name: 2026 Korean Mobile Smishing SIEM Dataset size_categories:
  • 1K<n<10K

๐Ÿ›ก๏ธ KR-Mobile-Smishing-SIEM-Benchmark-2026

"2026๋…„ํ˜• ํ•œ๊ตญ ๋ชจ๋ฐ”์ผ ์Šค๋ฏธ์‹ฑ ํ‚ฌ์ฒด์ธ์„ SIEM ํ™˜๊ฒฝ์—์„œ ํƒ์ง€ยทํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•œ ํ•ฉ์„ฑ ๋ฒค์น˜๋งˆํฌ ๋ฐ์ดํ„ฐ์…‹"

๋ฐฐ๊ฒฝ

๊ธฐ์กด ์Šค๋ฏธ์‹ฑ ํƒ์ง€ ๋ฐ์ดํ„ฐ์…‹์€ SMS ํ…์ŠคํŠธ ๋ถ„๋ฅ˜์— ํ•œ์ •๋˜์–ด, SIEM ํ™˜๊ฒฝ์—์„œ์˜ ๋‹ค๋‹จ๊ณ„ ๊ณต๊ฒฉ ํƒ์ง€(๋ฆฌ๋””๋ ‰ํŠธ ์ฒด์ธ, ํผ ํƒˆ์ทจ, MFA ํ”ผ๋กœ ๊ณต๊ฒฉ ๋“ฑ)๋ฅผ ํ‰๊ฐ€ํ•  ์ˆ˜ ์—†์—ˆ์Šต๋‹ˆ๋‹ค. ์ด ๋ฐ์ดํ„ฐ์…‹์€ SMS ์ˆ˜์‹ ๋ถ€ํ„ฐ ๊ธˆ์œต ํ”ผํ•ด ์ฐจ๋‹จ๊นŒ์ง€์˜ ์ „์ฒด ํ‚ฌ์ฒด์ธ์„ SIEM ๋กœ๊ทธ ํ˜•ํƒœ๋กœ ์žฌํ˜„ํ•ฉ๋‹ˆ๋‹ค.

์ œ์ž‘ ๋ฐฉ์‹

  • ํ™”์ดํŠธํ•ด์ปค ํŽ˜๋ฅด์†Œ๋‚˜๋ฅผ ํƒ‘์žฌํ•œ AI ๊ธฐ๋ฐ˜ ์‹œ๋‚˜๋ฆฌ์˜ค ์„ค๊ณ„ + ์‚ฌ๋žŒ ์ง์ ‘ ๊ฒ€์ˆ˜ (Human-in-the-Loop)
  • MITRE ATT&CK for Mobile (v16+) ํ”„๋ ˆ์ž„์›Œํฌ ๊ธฐ๋ฐ˜ ๋ผ๋ฒจ๋ง
  • 5๊ฐœ ์ƒ๊ด€๋ถ„์„ ํƒ์ง€ ๊ทœ์น™์— ๋Œ€ํ•œ ์ •๋‹ต์ง€(rule_fire_verification) ํฌํ•จ
  • RFC 2606 / RFC 5737 ์ค€์ˆ˜: ๋ชจ๋“  ๋„๋ฉ”์ธยทIPยท์ „ํ™”๋ฒˆํ˜ธ ๋น„ํ™œ์„ฑํ™” ์ฒ˜๋ฆฌ

๐Ÿ“Š ๋ฐ์ดํ„ฐ ํŠน์ง• (Key Features)

Platform: iOS / Android ๋ชจ๋ฐ”์ผ ํ™˜๊ฒฝ

Scenarios: ๊ฒฐ์ œ ์‚ฌ์นญ, ๊ณต๊ณต๊ธฐ๊ด€ ์‚ฌ์นญ, ํƒ๋ฐฐ ์‚ฌ์นญ, ์ธ์ฆ์„œ ๊ฐฑ์‹  ์‚ฌ์นญ ๋“ฑ 10์ข…+

Case Types: malicious(๊ณต๊ฒฉ ์„ฑ๊ณต) / benign_lookalike(๊ณต๊ฒฉ ์ค‘๋‹จ) / benign(์ •์ƒ) 3์ข… ํ˜ผํ•ฉ โ†’ True Positive Rate์™€ False Positive Rate ๋™์‹œ ์ธก์ • ๊ฐ€๋Šฅ

Log Sources: messaging, network, browser_security, identity, financial, noise

Labels: ์ด๋ฒคํŠธ ๋‹จ์œ„ ground_truth(malicious/suspicious/benign) + MITRE ATT&CK tactic/technique ID

Detection Rules: SMISH_REDIRECT_CHAIN, PHISH_WARNING_BYPASS, SENSITIVE_FORM_SEQUENCE, PLATFORM_DIVERGENCE, POST_LURE_FINANCIAL_RISK_SIGNAL โ€” ๊ฐ ๊ทœ์น™์˜ ๋ฐœ๋™ ์—ฌ๋ถ€ยท๊ฒฝ๋กœยท๊ทผ๊ฑฐ ํฌํ•จ

Noise Model: ์‹ค์ œ ๊ธฐ์—… ํ™˜๊ฒฝ์„ ๋ชจ์‚ฌํ•œ ๋ฐฐ๊ฒฝ ํŠธ๋ž˜ํ”ฝ 8๊ฐœ ์นดํ…Œ๊ณ ๋ฆฌ, 200 ์ด๋ฒคํŠธ ์Šค์ผ€์ผ๋ง ์ง€์›

๐Ÿ“Š ๋ฐ์ดํ„ฐ์…‹ ๊ตฌ์„ฑ (Dataset Composition)

ํ•ญ๋ชฉ ๊ฐ’
Total Samples 3 (Preview) / 100+ (Full version available)
Case Types malicious, benign_lookalike, benign
Events per incident 8~15 (core) / 200 (with scaling template)
Time Range 2026
Format JSON
Language ํ•œ๊ตญ์–ด (Korean) + English metadata

๐Ÿ”’ ์•ˆ์ „์„ฑ (Safety & Compliance)

  • ๋ชจ๋“  ๋„๋ฉ”์ธ: .example TLD (RFC 2606) โ€” DNS ํ•ด์„ ๋ถˆ๊ฐ€
  • ๋ชจ๋“  IP: TEST-NET ๋ฒ”์œ„ (RFC 5737) โ€” ๋ผ์šฐํŒ… ๋ถˆ๊ฐ€
  • ๋ชจ๋“  ์ „ํ™”๋ฒˆํ˜ธ: ๋ฏธํ• ๋‹น ๋ฒˆํ˜ธ ๋Œ€์—ญ
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