44건 · 관련도순
Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Detecting compromised cloud credentials across AWS, Azure, and GCP by analyzing anomalous API activity, impossible travel patterns, unauthorized resource provisioning, and credential abuse indicators using GuardDuty, Defender for Identity, and SCC Event Threat Detection.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Detect anomalies in DNP3 (Distributed Network Protocol 3) communications used in SCADA systems by monitoring for unauthorized control commands, firmware update attempts, protocol violations, and deviations from baseline traffic patterns using deep packet inspection and machine learning approaches.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert en monitoring d'agents IA en temps réel (dashboards, alertes, détection d'anomalies, SLA tracking)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Detect anomalies in DNP3 (Distributed Network Protocol 3) communications used in SCADA systems by monitoring for unauthorized control commands, firmware update attempts, protocol violations, and deviations from baseline traffic patterns using deep packet inspection and machine learning approaches.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access patterns.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Deploys and configures Zeek (formerly Bro) network security monitor to passively analyze network traffic, generate structured logs, detect anomalous behavior, and create custom detection scripts for threat hunting and incident response.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Detecting compromised cloud credentials across AWS, Azure, and GCP by analyzing anomalous API activity, impossible travel patterns, unauthorized resource provisioning, and credential abuse indicators using GuardDuty, Defender for Identity, and SCC Event Threat Detection.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Signals scout that watches a PostHog project's most-viewed dashboards and insights for recent anomalies — sudden bursts, drops, flat-lines, and trend breaks at the daily or hourly level. It discovers what the team actually looks at (view counts, dashboard access), curates a durable watchlist in the scratchpad, and balances re-checking known high-value insights (exploit) against discovering new ones (explore) across runs, since no single run can cover a busy project. Anomalies are scored by robust deviation from each insight's own seasonality-matched baseline; it emits a finding only when a move clears the confidence bar, otherwise it updates the baseline memory and closes out empty. Self-contained peer in the signals-scout-* fleet.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use for sensemaking, frame revision, anomaly handling, questioning strategy, and ambiguity diagnosis with the Data/Frame model before action is chosen. NOT for deterministic classification, settled execution planning, or situations where the frame is already stable.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Great Expectations, dbt tests, anomaly detection, and data contracts for data quality. Activate on: data quality, data validation, Great Expectations, data contract, anomaly detection, SLA, freshness check, schema validation. NOT for: dbt model structure (use dbt-analytics-engineer), schema evolution (use schema-evolution-manager).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Deploys and configures Zeek (formerly Bro) network security monitor to passively analyze network traffic, generate structured logs, detect anomalous behavior, and create custom detection scripts for threat hunting and incident response.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Detect anomalies in DNP3 (Distributed Network Protocol 3) communications used in SCADA systems by monitoring for unauthorized control commands, firmware update attempts, protocol violations, and deviations from baseline traffic patterns using deep packet inspection and machine learning approaches.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access patterns.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Detecting compromised cloud credentials across AWS, Azure, and GCP by analyzing anomalous API activity, impossible travel patterns, unauthorized resource provisioning, and credential abuse indicators using GuardDuty, Defender for Identity, and SCC Event Threat Detection.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.