'forensics' 검색 결과

Examine file system slack space, MFT entries, USN journal, and alternate data streams to recover hidden data and reconstruct file activity on NTFS volumes.

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Uses the Linux Audit framework (auditd) with ausearch and aureport utilities to detect intrusion attempts, unauthorized access, privilege escalation, and suspicious system activity. Covers audit rule configuration, log querying, timeline reconstruction, and integration with SIEM platforms. Activates for requests involving auditd analysis, Linux audit log investigation, ausearch queries, aureport summaries, or host-based intrusion detection on Linux.

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Perform comprehensive forensic analysis of disk images using Autopsy to recover files, examine artifacts, and build investigation timelines.

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Detect and analyze heap spray attacks in memory dumps using Volatility3 plugins to identify NOP sled patterns, shellcode landing zones, and suspicious large allocations in process virtual address space.

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Parse and analyze email headers to trace the origin of phishing emails, verify sender authenticity, and identify spoofing through SPF, DKIM, and DMARC validation.

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Analyze Windows LNK shortcut files and Jump List artifacts to establish evidence of file access, program execution, and user activity using LECmd, JLECmd, and manual binary parsing of the Shell Link Binary format.

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Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.

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Android device offensive security, forensic extraction, and mobile app exploitation. Use this whenever the user needs to: break into or bypass the lock screen of an Android phone, extract data from a seized Android device for evidence, analyze or reverse-engineer an APK to find vulnerabilities, intercept communications on an Android device, exploit Android apps via deeplinks, steal crypto wallets or app data, set up rogue WiFi to target a phone, or plan any mobile device investigation or penetration test. Also trigger when the user mentions Android forensics, mobile pentesting, law enforcement phone extraction, device exploitation, WhatsApp or Telegram extraction from a phone, Android app reversing, CVE research for Android, or needing a chain of custody for mobile evidence. Even if the user describes the scenario casually or in another language, if they need to gain access to or pull data from an Android phone, use this skill.

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Use when performing cybersecurity operations - offensive or defensive - including threat detection and hunting, SOC operations, incident response, digital forensics and DFIR, malware analysis and reverse engineering, threat intelligence, cloud/container/network/application/API security, identity-access and zero-trust, OT/ICS and endpoint security, vulnerability and supply-chain management, DevSecOps, cryptography, AI security, compliance and governance, and red-team or penetration testing under signed rules of engagement; routes to one of 800+ technique playbooks mapped to MITRE ATT&CK, D3FEND, and NIST CSF.

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Evidenzbasierte Windows-Speicherbereinigung mit kontrolliertem Notfallmodus, physischer Belegungsmessung, Prozess- und Entstehungsforensik sowie reversiblen Maßnahmen gegen wiederkehrende Speicherfüller.

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Use when screenshots, websites, moodboards, competitors, or visual examples are provided and the task needs design DNA extraction, comparison, remixing, or translation into an original design direction.

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Operational guide for the NV oOS Dependabot alert triage-and-remediation loop: querying open alerts via gh api, separating stale from genuinely vulnerable lockfile entries (default-branch forensics), deciding between safe in-major override bumps, scoped overrides, or deferred major migrations (npm pack signature diffing), regenerating the 13-lockfile tree, dismissing alerts with the right reason, GitHub issue housekeeping, and triaging the known CI failure modes. Use when asked to fix dependabot alerts, close out dependabot issues, bump vulnerable npm packages, or resume a dependabot session.

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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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Görüntü steganografi — LSB/MSB, alpha kanal, metadata, file carving tespiti ve Python Pillow ile her kanalın bit-seviyesinde analizi.

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Windows/Linux memory dump, hiberfil.sys analizi — volatility3 ile process, credential, env var çıkarma, DPAPI master key kurtarma ve pypykatz entegrasyonu.

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PCAP/network capture analizi — Wireshark, tshark, Python scapy/pyshark ile SMTP/FTP/DNS/custom protokol çözme, gizli veri çıkarma.

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Ses steganografi — WAV LSB, DTMF decode, spektogram analizi, DeepSound, Morse code ve Python ile tam çözüm şablonları.

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FETIH Siber Güvenlik Ana Orkestratörü — 912+ skill ile tüm siber güvenlik alanlarını kapsar. Tehdit avı, zararlı yazılım analizi, dijital adli bilişim, olay müdahalesi, bulut güvenliği, ağ güvenliği, web uygulama güvenliği, API güvenliği, sızma testi, red team, SOC operasyonları, OT/ICS güvenliği ve daha 25+ alt kategori. ljagiello/ctf-skills ve Eyadkelleh/awesome-claude-skills-security entegre edildi.

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Detect lateral movement in network traffic using Zeek (formerly Bro) log analysis. Parses conn.log, smb_mapping.log, smb_files.log, dce_rpc.log, kerberos.log, and ntlm.log to identify SMB file transfers, Pass-the-Hash activity, remote service execution, and anomalous internal connections.

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Detects command-and-control (C2) communications tunneled through DNS protocol including DNS tunneling tools (Iodine, dnscat2, dns2tcp, Cobalt Strike DNS beacon), domain generation algorithms (DGA), encoded payload delivery via TXT/CNAME records, and DNS beaconing patterns. Covers Shannon entropy analysis of query subdomains, statistical anomaly detection, ML-based DGA classification, passive DNS correlation, and Zeek/Suricata signature development. Activates for requests involving DNS-based C2 detection, DNS tunnel identification, suspicious DNS traffic investigation, or DGA domain classification.

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Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.

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Examine file system slack space, MFT entries, USN journal, and alternate data streams to recover hidden data and reconstruct file activity on NTFS volumes.

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Extract and analyze Windows Registry hives to uncover user activity, installed software, autostart entries, and evidence of system compromise.

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Parse Windows LNK shortcut files to extract target paths, timestamps, volume information, and machine identifiers for forensic timeline reconstruction.

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