スキルを探す

Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with multi-source intelligence; or making block/monitor/whitelist decisions. Activates for requests involving VirusTotal, AbuseIPDB, MalwareBazaar, MISP, or IOC enrichment pipelines.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze the NTFS Master File Table ($MFT) with MFTECmd, analyzeMFT, and X-Ways Forensics to recover metadata and content of deleted files by examining MFT record entries, $LogFile, $UsnJrnl, and MFT slack space. Use when recovering evidence of deleted files, reconstructing NTFS file-system timelines, or detecting anti-forensic timestomping during a Windows forensic examination.

日本語の概要は準備中です。原文の説明を表示しています。

Captures and analyzes network packet data using Wireshark and tshark to identify malicious traffic patterns, diagnose protocol issues, extract artifacts, and support incident response investigations on authorized network segments.

日本語の概要は準備中です。原文の説明を表示しています。

Extract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys) online or offline using SharpDPAPI, SharpChrome, Mimikatz, or Impacket's dpapi.py, including domain-wide decryption via the DPAPI backup key. Use during authorized red-team credential-access engagements after gaining a foothold or when triaging DPAPI blobs pulled from a host.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom protocols to reverse-engineer beacon patterns, command structures, data encoding, and infrastructure (primary servers, fallback domains, dead drops). Use after reverse engineering reveals network traffic needing protocol analysis or when building detection signatures for a framework like Cobalt Strike, Metasploit, or Sliver.

日本語の概要は準備中です。原文の説明を表示しています。

Perform static analysis of Android APK malware using apktool for resource decompilation, jadx for Java source recovery, and androguard for manifest inspection, dangerous permission-combination detection, and identification of obfuscated code, dynamic code loading, and reflection-based API calls. Use to statically triage a suspicious APK without executing it or to build mobile malware detection rules.

日本語の概要は準備中です。原文の説明を表示しています。

URLScan.io is a free service for scanning and analyzing suspicious URLs. It captures screenshots, DOM content, HTTP transactions, JavaScript behavior, and network connections of web pages in an isolat

日本語の概要は準備中です。原文の説明を表示しています。

Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems.

日本語の概要は準備中です。原文の説明を表示しています。

Runtime iOS app security testing with Objection (Frida): inspect keychain and filesystem data, explore app internals at runtime, and validate/bypass client-side protections during authorized mobile assessments.

日本語の概要は準備中です。原文の説明を表示しています。

Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use when an incident investigation needs a defensible attribution confidence level.

日本語の概要は準備中です。原文の説明を表示しています。

Examine Linux system artifacts (auth logs, cron/systemd persistence, shell history, SSH keys, and system configuration) to uncover evidence of compromise, detect rootkits or backdoors, and reconstruct user/attacker activity. Use when investigating a compromised Linux server or workstation, hunting for persistence mechanisms, or scoping a Linux-based breach during incident response.

日本語の概要は準備中です。原文の説明を表示しています。

Analyzes network traffic generated by malware during sandbox execution or live incident response to identify C2 protocols, data exfiltration channels, payload downloads, and lateral movement patterns using Wireshark, Zeek, and Suricata. Activates for requests involving malware network analysis, C2 traffic decoding, malware PCAP analysis, or network-based malware detection.

日本語の概要は準備中です。原文の説明を表示しています。

Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis, volatile data examination, process injection detection, or memory-resident malware investigation.

日本語の概要は準備中です。原文の説明を表示しています。

Detect sandbox and VM evasion techniques in malware samples by analyzing timing checks, VM/hypervisor artifact queries, user-interaction checks, and sleep-inflation patterns from Cuckoo or AnyRun behavioral reports. Use when a sample shows no or minimal activity in a sandbox, when a behavioral report needs review for evasion indicators, or when building detections for anti-analysis techniques.

日本語の概要は準備中です。原文の説明を表示しています。

Query the Malpedia API to look up malware family aliases and naming (platform.family_name), pull community/vendor YARA rules, link families to threat actors, and map family relationships such as loader-payload chains and shared authorship. Use when researching a malware family's aliases, lineage, or actor attribution, or when sourcing YARA rules for detection.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze Windows LNK shortcut files and Jump List artifacts with LECmd, JLECmd, and manual Shell Link Binary Format parsing to establish evidence of file access, program execution, and user activity that persists even after the target file is deleted. Use when investigating Windows user activity, reconstructing file-access or program-execution timelines, or examining recent/frequently-used file evidence in a forensic exam.

日本語の概要は準備中です。原文の説明を表示しています。

Detect kernel-level rootkits in Linux memory dumps using Volatility3 linux plugins (check_syscall, lsmod, hidden_modules), rkhunter system scanning, and /proc vs /sys discrepancy analysis to identify hooked syscalls, hidden kernel modules, and tampered system structures.

日本語の概要は準備中です。原文の説明を表示しています。

Investigate compromised Docker containers by analyzing images, layers, volumes, logs, and runtime artifacts to identify malicious activity and evidence.

日本語の概要は準備中です。原文の説明を表示しています。

Reverse engineer Go-compiled malware in Ghidra by parsing Go buildinfo and pclntab structures, recovering stripped/obfuscated function names (e.g. via GoResolver), and extracting embedded module/dependency strings and types from Go binaries. Use when analyzing a Go-language malware sample, deobfuscating a garble-packed Go binary, or recovering function names and third-party dependencies from a stripped Go executable.

日本語の概要は準備中です。原文の説明を表示しています。

Analyzes intrusion activity against the Lockheed Martin Cyber Kill Chain framework to identify which phases an adversary has completed, where defenses succeeded or failed, and what controls would have interrupted the attack at earlier phases. Use when conducting post-incident analysis, building prevention-focused security controls, or mapping detection gaps to kill chain phases. Activates for requests involving kill chain analysis, intrusion kill chain, attack phase mapping, or Lockheed Martin kill chain framework.

日本語の概要は準備中です。原文の説明を表示しています。

Parse and analyze Cobalt Strike Malleable C2 profiles with dissect.cobaltstrike (profiles and beacon-payload configs) and pyMalleableC2 (AST parsing) to extract HTTP/DNS transforms, URIs, headers, sleep/jitter, and injection behavior, then generate network detection signatures. Use when reverse-engineering a captured malleable profile or building detections against Cobalt Strike Beacon traffic.

日本語の概要は準備中です。原文の説明を表示しています。

Detect abnormal access in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics for after-hours bulk downloads, new-IP access, and API-call spikes (e.g. GetObject) via statistical baselines and time-series anomaly detection. Use when investigating suspected cloud data exfiltration or building related detection rules.

日本語の概要は準備中です。原文の説明を表示しています。

Monitor Certificate Transparency logs using crt.sh and Certstream to detect phishing domains, lookalike certificates, and unauthorized certificate issuance targeting your organization.

日本語の概要は準備中です。原文の説明を表示しています。