「event」の検索結果

Detect abuse of elevation control mechanisms (T1548), including Windows UAC bypass via auto-elevating binaries like fodhelper.exe and Linux sudo/setuid/setgid exploitation, by monitoring registry changes, integrity-level transitions, and parent-child process relationships via Sysmon and Windows Security events. Use when hunting privilege-escalation activity or validating elevation-abuse detection coverage.

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Detect DCSync attacks (MITRE T1003.006) where adversaries abuse Active Directory replication privileges to extract password hashes, by auditing Event ID 4662 for the DS-Replication-Get-Changes GUIDs and flagging non-domain-controller accounts issuing DsGetNCChanges RPC calls. Use when hunting for credential theft via Mimikatz lsadump::dcsync or Impacket secretsdump, investigating lateral movement with domain admin credentials, or auditing AD replication permissions.

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Detect Kerberos Golden Ticket forgery (e.g. Mimikatz-forged tickets) by analyzing Windows Event ID 4769 for RC4 encryption downgrades (0x17), abnormal ticket lifetimes, and krbtgt account anomalies using Splunk and Elastic SIEM queries. Use when investigating suspected forged Kerberos tickets or krbtgt credential theft in Active Directory.

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Detect LSASS credential dumping, SAM database extraction, and NTDS.dit theft (e.g. via Mimikatz) using Sysmon Event ID 10 process-access logging, Windows Security logs, and SIEM correlation rules. Use when hunting for credential-theft activity on Windows/Active Directory hosts or triaging EDR alerts on LSASS access.

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Detect compromised cloud credentials across AWS, Azure, and GCP by analyzing anomalous API activity, impossible-travel patterns, and credential-stuffing indicators using GuardDuty, Microsoft Defender for Identity, and Google SCC Event Threat Detection. Use when investigating alerts about cloud API activity from unfamiliar locations, responding to an exposed-credential notification, or scoping a credential compromise.

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Detect Golden Ticket attacks in Active Directory using Splunk and KQL queries against domain controller event logs, looking for Kerberos TGT anomalies such as mismatched encryption types, impossible ticket lifetimes, non-existent accounts, and forged PAC signatures. Use when hunting for Kerberos ticket forgery or krbtgt-based persistence (MITRE T1558.001) in AD environments.

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Detect and prevent dependency confusion (public-over-private package name resolution) in npm, PyPI, and Maven by enumerating claimable internal package names with tools like `confused` and OWASP `dep-scan`, then enforcing source restrictions via `.npmrc`, `pip.conf`/`pyproject.toml`, and Maven `settings.xml`. Use when onboarding a repo to a supply-chain security program, auditing lockfiles/manifests for confusable dependencies, or after an incident that may have leaked internal package names.

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Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests (Event ID 4769) targeting service accounts with SPNs, which attackers request offline to crack service account passwords. Use when hunting for MITRE T1558 credential access activity or investigating suspected service account password cracking attempts in Active Directory Kerberos logs.

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Deploy and operationalize Amazon GuardDuty, covering protection plans for S3, EKS, EC2 runtime monitoring, and Lambda, interpreting finding severity, and building automated response with EventBridge and Lambda. Use when establishing threat detection for AWS accounts, investigating findings on compromised instances or credential abuse, or building automated incident-response playbooks.

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Deploys deception-based honeytokens in Active Directory including fake privileged accounts with AdminCount=1, fake SPNs for Kerberoasting detection (honeyroasting), decoy GPOs with cpassword traps, and fake BloodHound paths. Monitors Windows Security Event IDs 4769, 4625, 4662, 5136 for honeytoken interaction. Use when implementing AD deception defenses for detecting lateral movement, credential theft, and reconnaissance.

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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.

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Configures microsegmentation policies to enforce least-privilege workload-to-workload access using tools such as VMware NSX, Illumio, and Calico, preventing lateral movement in zero trust architectures. Use when designing or implementing network microsegmentation as part of a zero trust architecture aligned with NIST SP 800-207.

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Generate forensic super-timelines with Plaso's log2timeline.py, pinfo.py, psort.py, and psteal.py CLI tools (fusing file-system MACB, registry, EVTX, browser history, prefetch, LNK, and more), then triage and filter the results in Timesketch. Use when reconstructing the full sequence of events on a compromised or forensically imaged host during a DFIR investigation.

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Deploy MISP, configure threat feeds (MISP community, freetext, TAXII, CSV), and use the PyMISP API to programmatically fetch, add, and search events and IOCs, building automated collection pipelines that aggregate indicators from community and commercial sources. Use when gathering, storing, or correlating IOCs and threat intelligence, or when scripting MISP ingestion via PyMISP.

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Plan and execute a comprehensive, MITRE ATT&CK-aligned red team engagement spanning threat modeling, reconnaissance, initial access, and post-exploitation to evaluate an organization's detection, prevention, and response against APT-style behavior. Use when scoping or running a full-scope, objective-based engagement, or purple-teaming against a specific threat actor's TTPs.

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Conduct a defensible cybersecurity risk assessment using the NIST SP 800-30 Rev 1 methodology: prepare scope and a risk model, identify threat sources and threat events, identify vulnerabilities and predisposing conditions, determine likelihood and impact, compute risk, and communicate results as a prioritized risk register. Use when an organization needs an actual risk *assessment* (not a maturity score), when a control framework (CSF, ISO 27001, RMF, SOC 2, PCI) requires a documented risk analysis as input, when leadership asks "what are our top risks and how bad are they", when assessing risk for a new system or major change, or when building a risk register from scratch. This is the methodology that feeds framework selection, ATO packages, and treatment decisions. Keywords: risk assessment, NIST 800-30, threat modeling, likelihood and impact, risk register, risk analysis, threat sources, vulnerabilities, risk determination, qualitative risk, risk matrix, residual risk, risk treatment.

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Build automated IOC enrichment pipelines in Splunk Enterprise Security by ingesting threat feeds into KV Store collections and correlating them against security events via lookup tables, modular inputs, and the Threat Intelligence Framework. Use when wiring threat intel into Splunk correlation searches to flag IOC matches and cut SOC triage time.

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Correlate Windows registry keys (USBSTOR, MountedDevices), Event Logs, and setupapi.dev.log to reconstruct USB device connection history, first/last-plugged timestamps, and drive letter mappings. Use when investigating removable media usage, tracking device provenance, or building a timeline for suspected data exfiltration.

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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.

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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, and builds SIEM detection rules from the event patterns. Use when investigating a suspected cluster compromise, reconstructing what an attacker did through the API server, or writing Kubernetes-specific detection content. Keywords: audit policy, audit log, kube-apiserver, exec into pod, RBAC change, anonymous access, detection rules. Do not use for syscall-level detection inside a running container - use detecting-container-runtime-threats-with-falco. '

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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.

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Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization.

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Use when working with *.excalidraw or *.excalidraw.json files, user mentions diagrams/flowcharts, or requests architecture visualization - delegates all Excalidraw operations to subagents to prevent context exhaustion from verbose JSON (single files: 4k-22k tokens, can exceed read limits)

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When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," or "tracking plan." For A/B test measurement, see ab-test-setup.

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