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概要と使いどころ
Detect Mimikatz credential-dumping activity via command-line pattern matching, LSASS access signatures, binary/hash indicators, and in-memory detection of known Mimikatz modules. Use when threat hunting for T1003 credential access, triaging EDR/SIEM alerts on LSASS access, scoping compromise during incident response, or validating detection coverage in a purple team exercise.
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 WMI event subscription persistence (MITRE T1546.003) by analyzing Sysmon Event IDs 19, 20, and 21 for malicious EventFilter, EventConsumer, and FilterToConsumerBinding creation, cross-referenced against root\subscription namespace contents and Sysinternals Autoruns. Use when hunting WMI-based persistence, triaging a Sysmon alert on these event IDs, or during incident response and purple-team validation of WMI defenses.
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 abuse of service accounts by hunting for anomalous interactive logons, privilege escalation, and lateral movement using EDR/SIEM telemetry (CrowdStrike Falcon, Microsoft Defender, Splunk, Elastic Security, Sysmon, Velociraptor) and Sigma detection rules. Use when hunting for service-account misuse or investigating a service account performing unexpected interactive logons.
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 privilege escalation attempts across Windows and Linux, including access token manipulation, UAC bypass, unquoted service path abuse, kernel exploits, and sudo/doas abuse. Use when threat hunting for T1068-style privilege escalation, triaging EDR/SIEM alerts on suspicious privilege changes, scoping compromise during incident response, or validating detection coverage in a purple team exercise.
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 risky OAuth application consent grants in Azure AD / Microsoft Entra ID using Microsoft Graph API, audit logs, and permission analysis to identify illicit consent grant attacks.
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 and respond to OAuth token theft and replay in Microsoft Entra ID (Azure AD), covering access token theft, refresh token replay, Primary Refresh Token (PRT) abuse, pass-the-cookie attacks, and Token Protection conditional access policies. Use for impossible-travel or anomalous token-usage alerts, suspected session hijacking, sign-in log analysis, or configuring token-binding defenses in Azure/M365.
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 and prevent QR code phishing (quishing) attacks that embed malicious URLs inside QR code images to bypass link-based email security, using image-based threat detection, OCR/QR decoding, and mobile-side scanning (Microsoft Defender for O365, Proofpoint TAP, Barracuda Multimodal AI). Use when configuring gateway rules against QR phishing or investigating suspicious emails containing QR codes.
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 process hollowing (MITRE T1055.012) by analyzing memory-mapped sections, hollowed process indicators, and parent-child anomalies using EDR telemetry, Volatility's malfind plugin, pe-sieve, Hollows Hunter, and Sysmon Event ID 25. Use when investigating a legitimate-looking process (svchost.exe, explorer.exe, rundll32.exe) suspected of hosting injected code via NtUnmapViewOfSection.
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 map and remove malware, backdoors, and attacker persistence mechanisms (registry Run keys, scheduled tasks, WMI subscriptions, services, cron/init.d) from infected Windows and Linux systems using Autoruns, EDR/AV, and YARA, restoring a clean state while preventing re-infection. Use after containment and forensic analysis have identified all compromised systems and persistence mechanisms and you are ready to eradicate and recover.
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。
Detects typosquatting attacks in npm and PyPI package registries by analyzing package name similarity using Levenshtein distance and other string metrics, examining publish date heuristics to identify recently created packages mimicking established ones, and flagging download count anomalies where suspicious packages have disproportionately low usage compared to their legitimate targets. The analyst queries the PyPI JSON API and npm registry API to gather package metadata for automated comparison. Activates for requests involving package typosquatting detection, dependency confusion analysis, malicious package identification, or software supply chain threat hunting in package registries.
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 AI-generated deepfake audio used in voice phishing (vishing) by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models, supporting batch audio analysis, confidence scoring, and forensic reporting. Use for deepfake voice detection, vishing investigations, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.
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。
Deploy AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based filters.
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。
Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to detect attackers moving between 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。
Detect adversary lateral movement across networks using Splunk SPL queries against Windows authentication logs, SMB traffic, and remote service (WMI/PsExec/RDP) abuse. Use when hunting for MITRE ATT&CK TA0008 lateral movement activity or investigating suspected pivoting between hosts during an incident, with Splunk as the SIEM.
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 Business Email Compromise (BEC) fraud, where attackers impersonate executives or vendors to trick employees into wire transfers or data disclosure without malicious links, using email gateway rules, behavioral analytics, and financial process controls. Use when investigating a suspected executive/vendor impersonation email or building detection rules for social-engineering fund-transfer fraud.
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 insider threat behavioral indicators including unusual data access, off-hours activity, mass file downloads, privilege abuse, and resignation-correlated data theft. Use when proactively threat-hunting for malicious or negligent insider activity, or when investigating a departing or disgruntled employee for potential data theft.
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。
Hardens LDAP directory services against credential harvesting, LDAP injection, anonymous binding, and channel-binding bypass by enforcing LDAPS, channel binding, and LDAP signing. Use when securing an LDAP or Active Directory environment against these attack classes or auditing directory service configurations for these vulnerabilities.
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。
Correlates security events in IBM QRadar SIEM using AQL (Ariel Query Language), custom rules, building blocks, and offense management to detect multi-stage attacks across network, endpoint, and application log sources. Use when SOC analysts need to investigate QRadar offenses, build correlation rules, or tune detection logic for reducing false positives.
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。
Correlates disparate security incidents, IOCs, and adversary behaviors across time and organizations to identify unified threat campaigns, attribute them to common threat actors, and extract shared indicators for improved detection. Use when multiple incidents exhibit overlapping indicators, when sector-wide attack campaigns require cross-organizational analysis, or when building campaign-level intelligence products. Activates for requests involving campaign analysis, incident clustering, cross-organizational IOC correlation, or MISP correlation engine.
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。
Configures Microsoft Defender for Endpoint (MDE) advanced protection settings including attack surface reduction rules, controlled folder access, network protection, and exploit protection. Use when hardening Windows endpoints beyond default Defender settings, deploying enterprise-grade endpoint protection, or meeting compliance requirements for advanced malware defense. Activates for requests involving Windows Defender configuration, ASR rules, MDE tuning, or Microsoft endpoint security.
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。
Run a targeted spearphishing simulation for initial access by developing OSINT-derived pretexts, building payloads (HTML smuggling, macro docs, ISO/LNK, OneNote, QR codes), standing up look-alike-domain email infrastructure with SPF/DKIM/DMARC via GoPhish, and tracking results. Use in authorized engagements to gain initial access via personalized phishing against specific individuals, distinct from broad campaigns.
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。
Conducts penetration testing of iOS and Android mobile applications following the OWASP Mobile Application Security Testing Guide (MASTG) to identify vulnerabilities in data storage, network communication, authentication, cryptography, and platform-specific security controls. The tester performs static analysis of application binaries, dynamic analysis at runtime, and API security testing to evaluate the complete mobile attack surface. Activates for requests involving mobile app pentest, iOS security assessment, Android security testing, or OWASP MASTG assessment.
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。
Plan, run, and measure an adversary engagement operation using the MITRE Engage framework so that deployed deception is driven by strategy instead of deployed ad hoc. Covers the Engage Matrix (Prepare, Expose, Affect, Elicit, Understand), the 10-Step Operational Process, mapping engagement Activities to the ATT&CK techniques they expose, and defining measurable Goals and Operational Objectives. Use when a team has honeypots, honeytokens, or canary tokens but no coordinating strategy, when leadership asks "should we engage attackers and how", when building a deception/denial program, when writing an adversary engagement operation plan, or when deciding which deception Activities to deploy against a specific threat actor. Keywords: MITRE Engage, adversary engagement, cyber deception strategy, denial and deception, Engage Matrix, EAC, EGO, Expose Affect Elicit, deception program, honeypot strategy, engagement operation.
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。
Deploy cloud-native deception across AWS, Azure, and GCP using decoy (honey) resources whose only purpose is to generate a high-fidelity alert the instant an attacker touches them: canary IAM access keys, permission-less decoy users/roles/service principals, honey object-storage buckets, and decoy secrets in Secrets Manager / Key Vault / Secret Manager. Wires detection through CloudTrail + EventBridge, Azure Sentinel honeytoken watchlists + Defender, and GCP Cloud Audit Logs, so any use of a decoy is routed to the SOC with near-zero false positives. Use when protecting cloud accounts and data stores, when an org has only on-prem honeypots and needs cloud coverage, when seeding fake AWS keys to catch credential theft and code-leak exposure, or when detecting cloud reconnaissance and lateral movement. Keywords: cloud deception, canary token AWS, honey S3 bucket, decoy IAM credentials, CloudTrail alert, GuardDuty, Sentinel honeytoken, decoy secret, honey service account, cloud honeypot, breach 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。