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Initial commit - 611 cybersecurity skills across all subdomains
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---
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name: analyzing-security-logs-with-splunk
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description: >
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Leverages Splunk Enterprise Security and SPL (Search Processing Language) to
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investigate security incidents through log correlation, timeline reconstruction,
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and anomaly detection. Covers Windows event logs, firewall logs, proxy logs, and
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authentication data analysis. Activates for requests involving Splunk investigation,
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SPL queries, SIEM log analysis, security event correlation, or log-based incident
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investigation.
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domain: cybersecurity
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subdomain: incident-response
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tags: [splunk, SPL, SIEM, log-analysis, security-monitoring]
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version: 1.0.0
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author: mahipal
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license: MIT
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---
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# Analyzing Security Logs with Splunk
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## When to Use
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- Investigating a security incident that requires correlation across multiple log sources
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- Hunting for adversary activity using known TTPs and IOCs
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- Building detection rules for specific attack patterns
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- Reconstructing an incident timeline from disparate log sources
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- Analyzing authentication anomalies, lateral movement, or data exfiltration patterns
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**Do not use** for real-time packet-level analysis; use Wireshark or Zeek for full packet capture analysis.
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## Prerequisites
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- Splunk Enterprise or Splunk Cloud with Enterprise Security (ES) app installed
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- Log sources ingested: Windows Event Logs (via Splunk Universal Forwarder or WEF), firewall, proxy, DNS, EDR, email gateway
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- Splunk CIM (Common Information Model) data models configured for normalized field names
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- SPL proficiency at intermediate level or higher
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- Role-based access with `search` and `accelerate_search` capabilities in Splunk
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## Workflow
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### Step 1: Scope the Investigation in Splunk
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Define search parameters based on incident triage data:
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```spl
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| Set initial investigation scope
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index=windows OR index=firewall OR index=proxy
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earliest="2025-11-14T00:00:00" latest="2025-11-16T00:00:00"
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(host="WKSTN-042" OR src_ip="10.1.5.42" OR user="jsmith")
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| stats count by index, sourcetype, host
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| sort -count
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```
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This query establishes which log sources contain relevant data for the investigation timeframe and affected assets.
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### Step 2: Analyze Authentication Events
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Investigate suspicious authentication patterns using Windows Security Event Logs:
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```spl
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| Detect brute force and credential stuffing
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index=windows sourcetype="WinEventLog:Security" EventCode=4625
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earliest=-24h
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| stats count as failed_attempts, values(src_ip) as source_ips,
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dc(src_ip) as unique_sources by TargetUserName
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| where failed_attempts > 10
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| sort -failed_attempts
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| Detect pass-the-hash (Logon Type 9 - NewCredentials)
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index=windows sourcetype="WinEventLog:Security" EventCode=4624
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Logon_Type=9
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| table _time, host, TargetUserName, src_ip, LogonProcessName
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| Detect lateral movement via RDP
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index=windows sourcetype="WinEventLog:Security" EventCode=4624
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Logon_Type=10
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| stats count, values(host) as targets by TargetUserName, src_ip
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| where count > 3
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| sort -count
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```
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### Step 3: Trace Process Execution
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Use Sysmon logs to reconstruct process execution chains:
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```spl
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| Process creation with parent chain (Sysmon Event ID 1)
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index=sysmon EventCode=1 host="WKSTN-042"
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earliest="2025-11-15T14:00:00" latest="2025-11-15T15:00:00"
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| table _time, ParentImage, ParentCommandLine, Image, CommandLine, User, Hashes
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| sort _time
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| Detect suspicious PowerShell execution
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index=sysmon EventCode=1 Image="*\\powershell.exe"
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(CommandLine="*-enc*" OR CommandLine="*-encodedcommand*"
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OR CommandLine="*downloadstring*" OR CommandLine="*iex*")
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| table _time, host, User, ParentImage, CommandLine
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| sort _time
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| Detect LSASS credential dumping
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index=sysmon EventCode=10 TargetImage="*\\lsass.exe"
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GrantedAccess=0x1010
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| table _time, host, SourceImage, SourceUser, GrantedAccess
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```
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### Step 4: Analyze Network Activity
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Correlate network logs with endpoint events:
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```spl
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| Detect C2 beaconing pattern
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index=proxy OR index=firewall dest_ip="185.220.101.42"
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| timechart span=1m count by src_ip
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| where count > 0
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| Detect DNS tunneling (high query volume to single domain)
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index=dns
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| rex field=query "(?<subdomain>[^\.]+)\.(?<domain>[^\.]+\.[^\.]+)$"
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| stats count, avg(len(query)) as avg_query_len by domain, src_ip
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| where count > 500 AND avg_query_len > 40
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| sort -count
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| Detect large data transfers (potential exfiltration)
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index=proxy action=allowed
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| stats sum(bytes_out) as total_bytes by src_ip, dest_ip, dest_host
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| eval total_MB=round(total_bytes/1024/1024,2)
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| where total_MB > 100
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| sort -total_MB
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```
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### Step 5: Build the Incident Timeline
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Reconstruct a unified timeline across all log sources:
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```spl
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| Unified incident timeline
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index=windows OR index=sysmon OR index=proxy OR index=firewall
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(host="WKSTN-042" OR src_ip="10.1.5.42" OR user="jsmith")
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earliest="2025-11-15T14:00:00" latest="2025-11-15T16:00:00"
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| eval event_summary=case(
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sourcetype=="WinEventLog:Security" AND EventCode==4624, "Logon: ".TargetUserName." from ".src_ip,
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sourcetype=="WinEventLog:Security" AND EventCode==4625, "Failed logon: ".TargetUserName,
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sourcetype=="XmlWinEventLog:Microsoft-Windows-Sysmon/Operational" AND EventCode==1,
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"Process: ".Image." by ".User,
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sourcetype=="proxy", "Web: ".http_method." ".url,
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1==1, sourcetype.": ".EventCode)
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| table _time, sourcetype, host, event_summary
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| sort _time
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```
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### Step 6: Create Detection Rules
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Convert investigation findings into persistent Splunk correlation searches:
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```spl
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| Correlation search: PowerShell spawned by Office applications
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index=sysmon EventCode=1
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Image="*\\powershell.exe"
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(ParentImage="*\\winword.exe" OR ParentImage="*\\excel.exe"
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OR ParentImage="*\\outlook.exe")
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| eval severity="high"
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| eval mitre_technique="T1059.001"
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| collect index=notable_events
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```
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## Key Concepts
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| Term | Definition |
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|------|------------|
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| **SPL (Search Processing Language)** | Splunk's query language for searching, filtering, transforming, and visualizing machine data |
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| **CIM (Common Information Model)** | Splunk's field normalization standard that maps vendor-specific field names to common names for cross-source queries |
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| **Notable Event** | An event in Splunk Enterprise Security flagged for analyst review based on a correlation search match |
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| **Data Model** | Structured representation of indexed data in Splunk enabling accelerated searches and pivot-based analysis |
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| **Sourcetype** | Classification label in Splunk that defines the format and parsing rules for a specific log type |
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| **Correlation Search** | Scheduled Splunk search that runs continuously and generates notable events when conditions are met |
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| **Timechart** | SPL command that creates time-series visualizations for identifying patterns, anomalies, and trends |
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## Tools & Systems
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- **Splunk Enterprise Security (ES)**: Premium SIEM application providing correlation searches, risk-based alerting, and investigation workbench
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- **Splunk SOAR**: Orchestration platform integrated with Splunk ES for automated response playbooks
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- **Sysmon**: Microsoft system monitoring tool providing detailed process, network, and file change telemetry ingested into Splunk
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- **Splunk Attack Analyzer**: Automated threat analysis that detonates suspicious files and URLs, feeding results into Splunk
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- **BOSS of the SOC (BOTS)**: SANS/Splunk training dataset for practicing incident investigation SPL queries
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## Common Scenarios
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### Scenario: Investigating Credential Stuffing Leading to Account Takeover
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**Context**: Security operations receives an alert for multiple successful logins to a single account from geographically dispersed IP addresses within a 30-minute window.
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**Approach**:
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1. Query Event ID 4624 for the affected account to map all login sources and times
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2. Correlate login IPs against threat intelligence feeds using a Splunk lookup table
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3. Check proxy logs for suspicious activity from the authenticated sessions
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4. Search for lateral movement from the compromised account (Event ID 4624 Type 3 to other hosts)
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5. Build a timeline showing credential stuffing attempts, successful login, and post-compromise activity
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6. Create a correlation search to detect similar patterns on other accounts
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**Pitfalls**:
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- Searching only the last 24 hours when the credential stuffing may have occurred over weeks
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- Not checking for VPN logs that may show the same account authenticating from impossible travel distances
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- Failing to normalize timestamps across log sources in different time zones
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## Output Format
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```
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SPLUNK INVESTIGATION REPORT
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============================
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Incident: INC-2025-1547
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Analyst: [Name]
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Investigation Period: 2025-11-14 00:00 UTC - 2025-11-16 00:00 UTC
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SEARCH SCOPE
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Indexes: windows, sysmon, proxy, firewall, dns
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Hosts: WKSTN-042, SRV-FILE01
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Users: jsmith, svc-backup
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Source IPs: 10.1.5.42, 10.1.10.15
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KEY FINDINGS
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1. [timestamp] - Initial compromise via phishing (Sysmon Event 1)
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2. [timestamp] - C2 established (proxy logs, beacon pattern detected)
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3. [timestamp] - Credential theft (Sysmon Event 10, LSASS access)
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4. [timestamp] - Lateral movement to SRV-FILE01 (Event 4624 Type 3)
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5. [timestamp] - Data staging and exfiltration (proxy bytes_out anomaly)
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SPL QUERIES USED
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[numbered list of key queries with descriptions]
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DETECTION GAPS IDENTIFIED
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- No Sysmon deployed on SRV-FILE01 (blind spot)
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- Proxy logs missing SSL inspection for C2 domain
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- PowerShell ScriptBlock logging not enabled
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RECOMMENDED DETECTIONS
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1. Correlation search for Office-spawned PowerShell
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2. Threshold alert for LSASS access patterns
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3. Behavioral rule for beacon-interval network traffic
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```
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