'detect' 검색 결과

Build Java applications that detect unusual patterns in time-series data with Azure AI Anomaly Detector. Covers single measurements, related signals, trend changes, model training, and checking detection results.

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• Detect unusual values in a single time series
• Check the latest incoming measurement for anomalies
• Find changes in time-series trends

Examine writing for familiar AI patterns, statistical features, Pangram detection scores, and hidden character signatures. Compare drafts against known human writing to interpret the evidence, with explanations and limitations rather than a definitive authorship claim.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check a post for common AI writing patterns
• Compare AI detection scores across drafts
• Compare a revision with known human writing

Build and test threat detection rules in Google SecOps using YARA-L 2.0, its rule language. The skill also checks existing coverage with simulated attack events, proposes rules for confirmed gaps, and deploys approved rules.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating detection rules from known attack behavior
• Tuning existing rules to reduce false positives
• Checking detection gaps against threat reports and CVE disclosures

Examine Windows endpoint logs for attempts to avoid security detection, including log clearing, altered file timestamps, injected code, and disabled protection. Correlate events to investigate suspicious behavior and develop detection rules with context for legitimate activity.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating cleared logs and altered file timestamps
• Checking suspected attempts to disable security tools
• Finding abuse of legitimate Windows utilities

Develop attack detection for SCADA systems that monitor and control industrial equipment. Compare network traffic and process measurements with normal behavior to identify unauthorized commands, altered sensor data, communication disruption, and indicators of known industrial malware.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Introducing intrusion detection in a SCADA environment
• Investigating suspected attacks on industrial control systems
• Creating detection rules for industrial protocol abuse

Create threat detection rules in Sigma, a shared format for security log searches, and convert them for Splunk, Elastic, or Microsoft Sentinel. Validate syntax, test expected matches and false positives, and map coverage to MITRE ATT&CK.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning threat reports into detection rules
• Reusing detection logic across SIEM platforms
• Testing rule matches and reducing false positives

Analyze Kubernetes API server audit logs to identify pod access, secret reads, permission changes, privileged pods, and anonymous requests. Use the event patterns to reconstruct suspicious activity and develop detection rules for security monitoring systems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Tracing API activity during a suspected cluster compromise
• Reviewing access to Kubernetes secrets
• Investigating permission changes and privileged pods

Compare Tika character encoding detection using one build and two configurations. Find files whose detected encoding changed, cross-check text quality signals, and inspect detector traces to investigate garbled text and identify which detector contributed to the result.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating encoding changes after a detector configuration update
• Finding files misclassified from Western encodings to CJK
• Tracing which detector selected a file's encoding

Volcengine Cloud Detect (云拨测) API integration for creating detection tasks, retrieving results, generating reports, and managing detection nodes. Use when the user wants to (1) create network detection/dialing tasks (拨测任务), (2) check task results or generate detection reports, (3) list or search detection nodes (拨测节点), (4) manage existing tasks (list/stop/restart/delete). Triggers include natural language requests about website monitoring, network latency testing, HTTP detection, ping tests, DNS tests, node queries, or report generation for Volcengine cloud detect service.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure AI Anomaly Detector SDK for Java workflow skill. Use this skill when the user needs Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build real-time detection for ransomware encryption by combining changes in data randomness with file modification rates, renames, and process activity. Define behavioral scores and response thresholds, then check detection accuracy and monitoring overhead.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building ransomware monitoring for endpoints and file servers
• Tuning detection thresholds to reduce false positives
• Investigating processes suspected of encrypting files

Build detections for fileless attacks on Windows using process activity, script logs, and endpoint monitoring. Investigate suspicious PowerShell, code injected into running processes, WMI persistence, and registry-based execution with logging guidance and detection query examples.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building detections for suspicious PowerShell activity
• Hunting for code injection into running processes
• Monitoring WMI persistence and registry-based execution

Create and tune Falco rules to detect possible container escapes, where a container gains access to its host. Define detection conditions, alert priorities, and output fields, then refine exceptions to reduce false positives.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Writing rules for suspicious host filesystem access
• Investigating noisy container escape alerts
• Reusing detection conditions through macros and lists

Build security detection rules in Splunk using SPL, its search language. Use examples for suspicious logins, network traffic, and PowerShell activity to develop searches, add context and severity, test historical logs, and measure false alerts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating rules to detect suspicious login activity
• Detecting large outbound transfers and access to multiple hosts
• Testing detection rules against historical logs

Evaluate object detection predictions against labeled ground truth with TAO Data Services. Produce class-level precision, recall, accuracy, and AP, a detection quality measure, with CSV results and precision–recall plots for reviewing model performance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Score object detection predictions against ground truth
• Review precision and recall by class
• Generate metrics CSV files and precision–recall plots

Implements technical breach detection capabilities including SIEM integration, DLP alert configuration, anomaly detection rules, and insider threat monitoring. Provides a breach classification taxonomy across confidentiality, integrity, and availability dimensions. Covers detection tool selection, alert tuning, and integration with privacy incident response workflows. Keywords: breach detection, SIEM, DLP, anomaly detection, insider threat, classification.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure AI Anomaly Detector SDK for Java workflow skill. Use this skill when the user needs Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

検査器（linter / ガード / CI ゲート / バリデータ / フィルタ）そのものの検出力が差分で静かに落ちていないか、また新設した検査が実際に検出できることが実証されているかを diff-time で検出する。Check 1 unverified detection reduction（検査器の検出ロジック（正規表現・除外パターン・解決集合・allowlist・baseline）を変更して検出件数が減ったのに、減った 1 件ずつが誤検出であった根拠が示されないまま「誤検出を潰した」「N → M 件に減少」と改善として主張している）、Check 2 unproven guard detection power（新しい検査ロジック・CI ゲート・バリデーションを追加・変更したのに、検出すべき入力を注入して検出されること／検出すべきでない入力で黙ることを示す変異注入の証跡が無く、「追加した」「CI が緑」を検出力の証拠として扱っている）、の 2 Check を対象とする report-only。テスト自身のアサーションが実質何も検証していない構造は test-assertion-effectiveness、レビュー基準・品質ゲートの明示的な弱体化（ルールの削除・閾値の引き下げ・suppression entry 追加・lint ルールの無効化）は review-criteria-integrity、リファクタ完了主張一般の検証は refactor-claim-audit、workflow の permissions と action pin は gha-workflow-security、設定ファイルの構文・型の妥当性は config-json へ委譲する

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create rules for Suricata and Snort, systems that detect suspicious network traffic, to flag port scans and reconnaissance. Combine packet patterns with connection counts, tune alert volume, and summarize scanning sources, targets, and activity.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create rules to detect Nmap and Masscan activity
• Monitor scanning within an internal network
• Tune alerts for authorized vulnerability scanners

Develop network monitoring for signs that may precede ransomware encryption. Uses detection rules, linked log events, and threat intelligence to identify suspicious activity and define how alerts should be investigated, prioritized, and escalated.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating detection rules for activity before ransomware encryption
• Linking suspicious logins, scans, and file share access
• Investigating unusual internal network communications

Detect attacks that misuse legitimate Windows utilities such as certutil and mshta. Analyze process logs, command arguments, and parent-child relationships with Sigma detection rules, then report suspicious events with severity scores and supporting event details.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating suspicious use of built-in Windows utilities
• Creating Sigma rules for unusual command arguments
• Finding Office apps that launch unexpected system utilities

Investigate malicious code placed inside legitimate Windows processes using memory analysis, event logs, and API monitoring. Identify injection methods, examine extracted code, and produce evidence reports and detection rules for suspicious process behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating unexpected connections from legitimate processes
• Finding injected code in memory dumps
• Classifying process hollowing and other injection methods

Set up decoy credentials, storage, and secrets across AWS, Azure, and GCP to detect suspicious access. Connect audit logs to security alerts, verify detection, and document permissions, resource inventories, and maintenance plans.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Detecting use of leaked cloud credentials
• Monitoring suspicious access to decoy storage and secrets
• Extending on-premises deception coverage to cloud environments

Deploy and configure Snort 3 to detect suspicious network traffic. Create custom detection rules, tune community rules to reduce false alerts, test them against captured traffic, and forward alerts to a central security monitoring platform.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploy intrusion detection at network boundaries
• Write rules for organization-specific threats
• Reduce noisy or false alerts