“log-analysis”的搜索结果

Security log parsing, anomaly detection, SIEM query building, Sigma rule creation, and correlation rule development across Splunk, Elastic, QRadar, and Microsoft Sentinel

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Perform forensic analysis of the macOS Unified Logging System (ULS) to investigate system events, application crashes, kernel panics, and potential indicators of compromise (IoCs) such as persistence mechanisms or unauthorized access.

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Implementing AWS CloudTrail log analysis for security monitoring, threat Tespit, and forensic investigation using Athena, CloudWatch Logs Insights, and SIEM integration to identify unauthorized access, privilege escalation, and suspicious API activity.

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Analyze ModSecurity, AWS WAF, and Cloudflare logs for SQL injection, attempts to manipulate database queries through web requests. Groups related requests, tracks source IPs, classifies attack methods, and produces incident reports.

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• Investigating suspected SQL injection attempts
• Tracking repeated attacks from the same source
• Connecting requests across an attack campaign

Examine Zeek connection logs for regular callbacks that may indicate communication with an attacker’s control server. Use ZAT and Pandas to group connections, measure timing variation, and identify source and destination pairs for further investigation.

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• Hunting for periodic callbacks in Zeek connection logs
• Comparing connection timing across source and destination pairs
• Developing beaconing detection rules and hunting queries

Analyze Apache and Nginx access logs for attack attempts, scanner activity, and repeated login requests. Add geographic context to source IP addresses and produce a prioritized findings report to support intrusion investigations and monitoring reviews.

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• Investigating suspicious requests in web access logs
• Finding injection attempts and unauthorized file access attempts
• Identifying scanners and repeated login attempts

Investigate security incidents in Splunk by connecting authentication, endpoint, and network logs. Use SPL, Splunk’s search language, to identify suspicious activity, reconstruct event timelines, document findings, and develop detection rules.

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• Investigating suspicious logins across multiple log sources
• Reconstructing an incident timeline
• Tracing suspicious processes and outbound traffic

Analyze access logs from AWS API Gateway, Kong, and Nginx with pandas, a Python data analysis library. Examine resource access patterns, authentication failures, and suspicious requests to investigate API abuse and inform detection rules.

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• Investigating sequential access to resource IDs
• Examining spikes in authentication failures
• Checking for rate limit bypass and injection attempts

Examine Linux authentication logs, shell history, SSH keys, scheduled tasks, and system settings to investigate a compromise. Help reconstruct activity, identify ways attackers maintain access, and document suspicious accounts, files, and configuration changes.

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• Investigating a potentially compromised Linux server
• Finding mechanisms that maintain unauthorized access
• Tracing user activity through logs and command history

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.

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

Full-lifecycle blog engine with 31 sub-skills, 12 templates, 100-point scoring, and 5 agents. Routes requests to the right sub-skill: writing, rewriting, analysis, outlines, audits, schema, charts, images, repurposing, AI citation SEO, FLOW prompts, topic clusters, and multilingual publishing. Optimized for Google rankings, E-E-A-T, and AI citations. Supports any platform. Use when the user asks to create, revise, analyze, plan, translate, localize, audit, or repurpose blog content, or invokes a /blog subcommand. Do not activate merely because the word "blog" appears in an unrelated coding, theme-debugging, database, log-analysis, or definition request.

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日志分析和排障时使用。适用于生产问题定位、慢查询排查、错误追踪、日志搜索。融合 ELK / Loki + 结构化日志 + Trace ID 追踪。

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Structured debug log analysis for Claude Code sessions — auto-discovers most recent log, runs reducer, extracts error patterns, correlates with full log, produces observability report. Fills 5 identified gaps: hook error body capture, agent identity, file path tracking, stall correlation, success visibility.

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Comprehensive SEO analysis for any website or business type. Orchestrates 52 sub-skills (52 active): full site audits, technical SEO (CWV/INP), schema, E-E-A-T content quality, images, sitemaps, GEO for AI Overviews, llms.txt generation, AI crawler audit, plus live GSC and Ahrefs MCP data. GSC: overview, drops, opportunities, brand-vs-nonbrand, cannibalization, compare, content-decay, index-issues, new-keywords. Ahrefs: overview, backlinks, keywords, competitors, content-gap, broken-links, new-links, anchor-analysis, dr-history, top-pages. Cross-MCP: serp, content-brief, brand-radar, site-audit-pro, report. Local: log-analysis, ai-content-check, internal-links, local, migration-check, obsidian-kb. AI Readability: llms-txt, robots-ai. Performance: lighthouse-audit, core-web-vitals, page-experience. Quality & AI Search: rich-results, ai-optimization, eeat-audit, helpful-content. Triggers on: "SEO", "audit", "schema", "Core Web Vitals", "sitemap", "E-E-A-T", "AI Overviews", "GEO", "technical SEO", "content quality", "structured data", "GSC", "Ahrefs", "backlinks", "keywords", "search console", "domain rating", "local SEO", "migration", "lighthouse", "pagespeed", "LCP", "INP", "CLS", "page experience", "interstitials", "mobile friendly", "rich results", "rich snippets", "schema eligibility", "AI optimization", "AI search readiness", "EEAT", "trust signals", "helpful content", "people-first", "content quality gate".

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Analyze server log files for crawl budget insights. Reads Apache Combined/Common or Nginx access logs locally (no external calls). Classifies bot vs user traffic, identifies top crawled URLs, crawl frequency by path, and crawl budget concerns. Use when user says "log analysis", "crawl budget", "server logs", "bot traffic", "crawl frequency", "access log", "analyze logs".

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Full-lifecycle blog engine with 31 sub-skills, 12 templates, 100-point scoring, and 5 agents. Routes requests to the right sub-skill: writing, rewriting, analysis, outlines, audits, schema, charts, images, repurposing, AI citation SEO, FLOW prompts, topic clusters, and multilingual publishing. Optimized for Google rankings, E-E-A-T, and AI citations. Supports any platform. Use when the user asks to create, revise, analyze, plan, translate, localize, audit, or repurpose blog content, or invokes a /blog subcommand. Do not activate merely because the word "blog" appears in an unrelated coding, theme-debugging, database, log-analysis, or definition request.

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Analyze WAF (ModSecurity/AWS WAF/Cloudflare) logs to detect SQL injection attack campaigns. Parses ModSecurity audit logs and JSON WAF event logs to identify SQLi patterns (UNION SELECT, OR 1=1, SLEEP(), BENCHMARK()), tracks attack sources, correlates multi-stage injection attempts, and generates incident reports with OWASP classification.

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Performs statistical analysis of Zeek conn.log connection intervals to detect C2 beaconing patterns. Uses the ZAT library to load Zeek logs into Pandas DataFrames, calculates inter-arrival time standard deviation, and flags periodic connections with low jitter. Use when hunting for command-and-control callbacks in network data.

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Parse Apache and Nginx access logs to detect SQL injection attempts, local file inclusion, directory traversal, web scanner fingerprints, and brute-force patterns. Uses regex-based pattern matching against OWASP attack signatures, GeoIP enrichment for source attribution, and statistical anomaly detection for request frequency and response size outliers.

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Leverages Splunk Enterprise Security and SPL (Search Processing Language) to investigate security incidents through log correlation, timeline reconstruction, and anomaly detection. Covers Windows event logs, firewall logs, proxy logs, and authentication data analysis. Activates for requests involving Splunk investigation, SPL queries, SIEM log analysis, security event correlation, or log-based incident investigation.

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Parses Kubernetes API server audit logs (JSON lines) to detect exec-into-pod, secret access, RBAC modifications, privileged pod creation, and anonymous API access. Builds threat detection rules from audit event patterns. Use when investigating Kubernetes cluster compromise or building k8s-specific SIEM detection rules.

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Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.

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Examine Linux system artifacts including auth logs, cron jobs, shell history, and system configuration to uncover evidence of compromise or unauthorized activity.

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Leverages Splunk Enterprise Security and SPL (Search Processing Language) to investigate security incidents through log correlation, timeline reconstruction, and anomaly detection. Covers Windows event logs, firewall logs, proxy logs, and authentication data analysis. Activates for requests involving Splunk investigation, SPL queries, SIEM log analysis, security event correlation, or log-based incident investigation.

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