'anomali' 검색 결과

홍수, 사이클론, 가뭄, 폭염 등의 재해 정보를 가져와 국가, 심각도, 피해 인구를 확인합니다. 기후 이상과 관련 뉴스도 조사해 상황의 배경을 파악합니다.

• 진행 중인 홍수와 사이클론 확인
• 재해 심각도와 피해 인구 조사
• 기후 이상과 관련 뉴스 확인

Evaluate threat intelligence platforms, which collect and organize cyber threat information. Define requirements, compare integrations and operating costs, plan practical trials, and use weighted scoring to select a platform and outline its rollout.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing requirements for TIP procurement
• Comparing platforms before a TIP migration
• Checking whether an existing TIP meets team needs

Analyze Modbus industrial network traffic to identify unauthorized commands, write operations, and unusual polling intervals. Compare activity with normal traffic patterns and allowed operations, then report anomalies by type and severity.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating suspicious Modbus traffic
• Establishing normal polling patterns
• Defining allowed Modbus function codes

Analyze security logs through four agent roles that parse events, detect anomalies, classify risk, and write an incident report. Use nginx, authentication, or system logs to review suspicious activity and receive a risk summary with recommended actions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate suspicious access in nginx logs
• Analyze anomalies in authentication logs
• Classify risks found in security logs

Implement AI-powered anomaly detection for operational metrics using time series analysis (Isolation Forest, Prophet, LSTM), alert correlation, and root cause analysis. Reduce alert fatigue by intelligently identifying true anomalies in system metrics, logs, and traces. Use when operations teams are overwhelmed by alert volume, when detecting complex multi-metric anomalies beyond static thresholds, when seasonal patterns make thresholds ineffective, or when needing to predict issues proactively before they impact users.

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Cognitum Seed 기기의 측정 데이터에서 급증, 값 정체 등의 이상을 탐지하고 분류합니다. 높은 점수에는 격리를 제안하고 이상 유형과 점수를 기록합니다.

• 기기의 비정상적인 측정값 조사
• 카나리 배포 진행 전 점검
• 기기군의 상태 경보 원인 분류

IoT 기기 그룹에 펌웨어를 단계적으로 배포합니다. 소수 기기의 이상을 확인한 뒤 범위를 넓히며, 배포 상태 조회와 롤백도 관리합니다.

• 기기 그룹에 지정한 펌웨어 버전 배포
• 선행 배포 확인 후 배포 범위 확대
• 펌웨어 배포 롤백

Inspect DNP3 traffic, a communication protocol used in industrial control systems, for unauthorized sources and unexpected commands. The skill compares captured traffic with normal patterns and reports suspicious control actions, restarts, and file operations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating suspicious DNP3 control commands
• Checking substation traffic against normal patterns
• Preparing DNP3 intrusion detection rules

Prepare for payroll by forecasting cash, drafting overdue invoice reminders to address shortfalls, and checking timesheets for anomalies. Produce a validated payroll worksheet or stage approved inputs in Gusto, leaving final payroll submission to the owner.

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• Checking whether cash will cover the next payroll
• Prioritizing overdue invoices to address a payroll shortfall
• Reviewing timesheet anomalies before payroll

Write SQL that uses BigQuery’s built-in machine learning and generative AI for forecasting, anomaly detection, identifying drivers of change, searching by meaning, and classifying or summarizing text stored in your data.

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• Forecasting future values from time-series data
• Detecting anomalies and investigating drivers of change
• Searching documents by meaning

Investigate unusual transactions, sensor spikes, rare failures, and other suspicious records. The skill helps compare anomaly detection methods and thresholds, prepare records for human review, and plan checks for false alarms and missed anomalies.

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• Reviewing unusual transactions and possible fraud
• Investigating sensor spikes and rare failures
• Comparing anomaly detection methods and thresholds

Identify anomalous sessions using Agent Monitor data — cost outliers from the pricing engine, token anomalies (cache miss spikes, compaction baseline surges), unusual event type ratios (PreToolUse/PostToolUse gaps, APIError clusters), behavioral deviations from workflow intelligence (complexity score outliers, error propagation anomalies), and sessions with abnormal metadata (extreme turn_count, high thinking_blocks, zero turn_duration).

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Screens biomedical / life-science papers for signs of data fabrication, image manipulation, and statistical anomalies, using the detection techniques distilled from the field's canonical exposure platforms (PubPeer, Data Colada, Science Integrity Digest, For Better Science) and tools (ImageTwin/Proofig, statcheck, GRIM/GRIMMER, Problematic Paper Screener, Seek & Blastn). Use when asked to check a paper/figure for image duplication, blot splicing, impossible statistics, paper-mill or tortured-phrase signals, research integrity, or "is this data faked"; or when a user shares a figure, Western blot, supplementary dataset, or DOI and asks whether it looks manipulated. Reports observable anomalies as questions for clarification — it never accuses anyone of fraud.

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Use anomalib for anomaly-detection install, data/model selection, training and evaluation, deployment and inference, and benchmark pipelines.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This skill covers detecting anomalies in Modbus/TCP and Modbus RTU communications in industrial control systems. It addresses function code monitoring, register range validation, timing analysis, unauthorized client detection, and deep packet inspection for malformed Modbus frames. The skill leverages Zeek with Modbus protocol analyzers, Suricata IDS with OT rules, and custom Python-based detection using Markov chain models for normal Modbus transaction sequences.

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.

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 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.

This skill covers detecting anomalies in Modbus/TCP and Modbus RTU communications in industrial control systems. It addresses function code monitoring, register range validation, timing analysis, unauthorized client detection, and deep packet inspection for malformed Modbus frames. The skill leverages Zeek with Modbus protocol analyzers, Suricata IDS with OT rules, and custom Python-based detection using Markov chain models for normal Modbus transaction sequences.

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.

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Comprehensive dimension analysis and reporting for CJA. Use this skill whenever the user wants to analyze one or more dimensions — including cardinality, distribution/skew, trends, anomalies, data quality errors, comparisons, and forecasting. Also trigger when someone asks "what are the top values for...", "dimension health", "explore this dimension", "dimension dashboard", "dimension statistics", "data quality check on a dimension", "dimension cardinality", "dimension trends", "dimension skew", "dimension anomalies", "compare dimensions", or any similar request to understand what's inside a CJA dimension. Produces an interactive HTML dashboard or a markdown report. Works with the CJA MCP server.

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Evaluates and selects Threat Intelligence Platform (TIP) products based on organizational requirements including feed integration capability, STIX/TAXII support, workflow automation, analyst interface, and total cost of ownership. Use when conducting a TIP procurement, migrating between TIP solutions, or assessing whether the current TIP meets program maturity requirements. Activates for requests involving ThreatConnect, MISP, OpenCTI, Anomali, EclecticIQ, or TIP procurement decisions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This skill covers detecting anomalies in Modbus/TCP and Modbus RTU communications in industrial control systems. It addresses function code monitoring, register range validation, timing analysis, unauthorized client detection, and deep packet inspection for malformed Modbus frames. The skill leverages Zeek with Modbus protocol analyzers, Suricata IDS with OT rules, and custom Python-based detection using Markov chain models for normal Modbus transaction sequences.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.