「detecting」の検索結果

Scan source code repositories, CI/CD pipelines, and configuration files for exposed AWS credentials using TruffleHog, git-secrets, and AWS-native detection. Use when integrating secrets scanning into CI/CD, auditing repositories (including git history) for historically committed AWS keys, responding to a GuardDuty alert about credential use from an unexpected location, or verifying credential rotation removed all exposed keys.

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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.

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This skill teaches security teams how to detect and respond to unauthorized cryptocurrency mining operations in cloud environments. It covers identifying cryptomining indicators through compute usage anomalies, network traffic patterns to mining pools, GuardDuty CryptoCurrency findings, and runtime process monitoring on EC2, ECS, EKS, and Azure Automation workloads.

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Detect API enumeration attacks (BOLA/IDOR, OWASP API1:2023) by writing SIEM detection rules that flag sequential or UUID identifier iteration, parameter tampering, and mixed 200/401/403 response patterns from API gateway and WAF logs. Use when investigating suspected object-level authorization abuse, building threat-hunting queries for API access-control bypass, or hardening API logging/rate-limiting against enumeration.

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Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning models to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs. Activates for requests involving authentication anomaly detection, login behavior analysis, UEBA implementation, or suspicious sign-in investigation.

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Detect anomalies in DNP3 communications used in SCADA/ICS systems by monitoring unauthorized control commands, firmware update attempts, protocol violations, and deviations from baseline traffic using deep packet inspection and machine learning approaches. Use when securing energy-sector or other OT/ICS networks, investigating suspicious DNP3 master/outstation activity, or building an anomaly-based IDS for industrial control traffic.

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Detect compromised O365 and Google Workspace email accounts by analyzing Unified Audit Logs and Azure AD sign-in logs for impossible travel, inbox rule creation/deletion (Set-InboxRule, New-InboxRule), external mail forwarding rules, and unusual Microsoft Graph API access or OAuth token use. Use when investigating suspected business email compromise (BEC), account takeover, or mailbox persistence via malicious inbox rules.

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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.

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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.

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Detect Azure service principal abuse in Microsoft Entra ID using KQL detection queries (Sentinel/Splunk) against Azure AD Audit and Sign-in Logs, covering added credentials, privileged role assignment, admin consent bypass, and service principal enumeration. Use when investigating suspected privilege escalation or persistence via service principals, or building threat-hunting queries for Entra ID identity abuse.

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Audit Azure Blob and ADLS storage accounts for public access exposure, weak or long-lived SAS tokens, missing encryption at rest, disabled HTTPS-only traffic, and outdated TLS versions, using the azure-mgmt-storage Python SDK to generate a risk-scored report. Use when assessing an Azure subscription's storage accounts for misconfiguration, building cloud security posture checks, or investigating a suspected data exposure via public blob access.

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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 lateral movement in Azure AD/Entra ID environments using Microsoft Graph API audit logs, Azure Sentinel KQL hunting queries, and sign-in anomaly correlation to identify privilege escalation, token theft, and cross-tenant pivoting.

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Detects and analyzes fileless malware that operates entirely in memory using PowerShell, WMI, .NET reflection, registry-resident payloads, and living-off-the-land binaries (LOLBins) without writing traditional executable files to disk. Use for requests involving fileless threat detection, in-memory malware investigation, LOLBin abuse analysis, or WMI persistence examination.

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Detect DLL side-loading and search-order hijacking (MITRE T1574) where adversaries plant malicious DLLs for legitimate signed applications to load, by analyzing Sysmon Event ID 7 DLL-load events, checking signatures/hashes against known-good versions, and flagging path anomalies with EDR tools like CrowdStrike, MDE, or SentinelOne. Use when investigating EDR alerts on unsigned DLLs, hunting for APT persistence via trojanized applications, or triaging incidents involving DLL hijacking.

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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.

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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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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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Build automated AWS GuardDuty finding response pipelines using EventBridge and Lambda to trigger real-time incident response, automatically quarantine compromised resources, and route security notifications. Use when designing automated remediation playbooks for GuardDuty findings across VPC Flow Logs, CloudTrail, DNS, EKS, or S3 data events, or when reducing mean time to respond to cloud threats.

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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 and test for OWASP API3:2023 Broken Object Property Level Authorization (BOPLA), covering excessive data exposure in API responses and mass assignment via injected request-body properties. Use when reviewing API responses/requests for over-exposed or over-writable object fields, or building detection rules and test cases for property-level authorization gaps that object-level checks miss.

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Detects defense evasion techniques used by adversaries in endpoint logs including log tampering, timestomping, process injection, and security tool disabling. Use when investigating suspicious endpoint behavior, building detection rules for evasion tactics, or conducting threat hunting for stealthy adversary activity. Activates for requests involving evasion detection, defense evasion analysis, log tampering detection, or MITRE ATT&CK TA0005.

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Detect and defend against indirect prompt injection hidden in web pages, documents, and images consumed by an agent, via content extraction (HTML/PDF/OCR), normalization, and scanning with LLM Guard's PromptInjection scanner or Hugging Face Prompt Guard 2. Use when an agent ingests untrusted external content and you need to screen it for injected instructions before the LLM processes it.

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Detect data exfiltration via DNS tunneling (tools like iodine, dnscat2, dns2tcp) by analyzing query entropy, subdomain length, query volume to single domains, TXT/CNAME/NULL record abuse, and oversized response payloads using passive DNS monitoring and statistical/ML methods. Use when hunting for covert DNS-based data exfiltration or building a passive DNS anomaly detection capability.

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。