'splunk' 검색 결과

Investigate security incidents in Splunk by connecting authentication, endpoint, and network logs. Use SPL, Splunk’s search language, to identify suspicious activity, reconstruct event timelines, document findings, and develop detection rules.

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• Investigating suspicious logins across multiple log sources
• Reconstructing an incident timeline
• Tracing suspicious processes and outbound traffic

Hunt for lateral movement, where attackers move between networked systems, using Splunk searches. Connect Windows logins with process and network activity to identify unusual host relationships, trace movement paths, and document affected accounts and systems.

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• Tracing activity after credential theft
• Investigating unusual logins between hosts
• Finding new or unexpected host connections

Build Splunk pipelines that import threat intelligence and match security events against indicators such as malicious IP addresses, domains, and file hashes. Add source, confidence, and severity details so analysts have context when investigating matches.

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• Importing external threat feeds into Splunk
• Matching network and endpoint logs against threat indicators
• Adding threat and asset context to alerts

Analyze Windows event logs in Splunk to investigate suspicious logins, privilege changes, persistence, and movement between computers. Build detection searches and organize endpoint activity into timelines, with findings linked to MITRE ATT&CK attack techniques.

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• Investigating suspicious Windows logins
• Building detection searches in Splunk
• Creating incident timelines

Translates Splunk SPL queries into Axiom APL, the query language used to search and analyze data in Axiom. Covers command and function equivalents, time filters, type conversions, and common patterns for migration or learning.

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• Migrating Splunk queries to Axiom
• Converting log aggregation and error-rate queries
• Learning APL through familiar SPL patterns

Investigate stolen Kerberos tickets, authentication credentials that can be reused without a password, by correlating Windows security events. Build Splunk or Elastic detection rules for cross-host reuse, weaker encryption, and unusual request volumes, and report risk-scored findings.

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• Investigating suspected reuse of stolen Kerberos tickets
• Building Splunk or Elastic detection rules
• Finding unusual ticket request volumes

Build security detection rules in Splunk using SPL, its search language. Use examples for suspicious logins, network traffic, and PowerShell activity to develop searches, add context and severity, test historical logs, and measure false alerts.

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• Creating rules to detect suspicious login activity
• Detecting large outbound transfers and access to multiple hosts
• Testing detection rules against historical logs

Create threat detection rules in Sigma, a shared format for security log searches, and convert them for Splunk, Elastic, or Microsoft Sentinel. Validate syntax, test expected matches and false positives, and map coverage to MITRE ATT&CK.

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• Turning threat reports into detection rules
• Reusing detection logic across SIEM platforms
• Testing rule matches and reducing false positives

Build incident response dashboards that bring affected systems, containment progress, indicators of compromise, and response timelines into one view. Help security teams coordinate active incidents and prepare operational reports and executive briefings.

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• Coordinating an active security incident
• Tracking affected systems and containment
• Reviewing response timelines after an incident

Design and build Axiom dashboards from logs or metrics, the numerical measurements of system behavior. Create charts, filters, and layouts for monitoring and reporting, or migrate existing Splunk and Grafana panels while validating queries against available data.

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• Build a service health monitoring dashboard
• Create interactive filters for investigating logs
• Migrate dashboards from Splunk or Grafana

Investigate Active Directory authentication logs with Splunk and KQL queries to find signs of Golden Ticket attacks, which use forged Kerberos authentication tickets. Examine encryption mismatches, missing ticket requests, unusual lifetimes, and account inconsistencies.

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• Hunting for forged tickets after credential theft
• Investigating encryption mismatches in authentication logs
• Checking for persistent access after incident response

Investigate misuse of application identities in Microsoft Entra ID using audit and sign-in logs. Provides detection queries and investigation steps for suspicious credential additions, privileged role assignments, consent grants, and service principal enumeration.

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• Investigating suspicious additions of application credentials
• Reviewing privileged roles assigned to application identities
• Building detection rules for Entra ID identity abuse

Investigate forged Kerberos authentication tickets in Active Directory by checking Windows logs for unusual encryption, excessive ticket lifetimes, and missing authentication requests. Produce a JSON report of suspicious indicators with risk ratings.

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• Investigating suspected forged authentication tickets
• Checking for possible krbtgt credential theft
• Building detection rules from authentication logs

Examine Windows and Active Directory logs for attempts to extract passwords and other credentials. Identify suspicious memory access and database exports, correlate events with user and host context, and produce findings with severity ratings and detection queries.

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• Investigating alerts about suspicious LSASS access
• Finding signs of SAM or NTDS.dit extraction
• Creating detection queries for Splunk or Elastic

Set up MISP to collect threat feeds and centrally manage indicators such as suspicious IP addresses and domains. Configure automated synchronization, connect related findings, and export indicators to security monitoring platforms or blocklists.

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• Centralizing threat indicators from multiple feeds
• Deploying MISP with Docker
• Automating threat feed synchronization

Leverages Splunk Enterprise Security and SPL (Search Processing Language) to investigate security incidents through log correlation, timeline reconstruction, and anomaly detection. Covers Windows event logs, firewall logs, proxy logs, and authentication data analysis. Activates for requests involving Splunk investigation, SPL queries, SIEM log analysis, security event correlation, or log-based incident investigation.

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Leverages Splunk Enterprise Security and SPL (Search Processing Language) to investigate security incidents through log correlation, timeline reconstruction, and anomaly detection. Covers Windows event logs, firewall logs, proxy logs, and authentication data analysis. Activates for requests involving Splunk investigation, SPL queries, SIEM log analysis, security event correlation, or log-based incident investigation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Leverages Splunk Enterprise Security and SPL (Search Processing Language) to investigate security incidents through log correlation, timeline reconstruction, and anomaly detection. Covers Windows event logs, firewall logs, proxy logs, and authentication data analysis. Activates for requests involving Splunk investigation, SPL queries, SIEM log analysis, security event correlation, or log-based incident investigation.

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Analyze DNS logs to find signs of hidden data transfers. Use query length, character randomness, traffic volume, and response sizes to identify suspicious domains and support monitoring rules with Python, Suricata, and Splunk examples.

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• Investigating possible data theft through DNS traffic
• Ranking suspicious domains found in DNS logs
• Creating DNS tunneling detection rules

Create detection rules and investigation queries from API access logs to flag repeated identifier probing and unusual authorization responses. Helps investigate attempts to access other users’ data and review logging and rate limits.

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• Investigating suspected unauthorized API data access
• Creating detection rules for repeated identifier probing
• Reviewing API logging and rate limits

Deploy Suricata to detect suspicious network traffic or block it inline. Configure protocol inspection, detection rules, and structured EVE JSON logs, then connect logs to analysis platforms and check packet drops and alert quality during operation.

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• Deploying network intrusion monitoring
• Configuring inline blocking of malicious traffic
• Adding custom detection rules and reducing noisy alerts

Analyze DNS query logs for signs of hidden data transfers and attacker command channels. This skill uses unusual name lengths, randomness, and traffic volumes, then correlates findings with endpoint processes and estimates possible data loss.

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• Investigating suspected data theft through DNS
• Finding algorithmically generated domains used by malware
• Reviewing hosts with unusually high DNS query volumes

Detect adversary lateral movement across networks using Splunk SPL queries against Windows authentication logs, SMB traffic, and remote service abuse.

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Build effective detection rules using Splunk Search Processing Language (SPL) correlation searches to identify security threats in SOC environments.

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