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Log Analysis & SIEM Integration

Security log parsing, anomaly detection, SIEM query building, Sigma rule creation, and correlation rule development across Splunk, Elastic, QRadar, and Microsoft Sentinel

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Security log parsing, anomaly detection, SIEM query building, Sigma rule creation, and correlation rule development across Splunk, Elastic, QRadar, and Microsoft Sentinel

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Log Analysis & SIEM Integration

Purpose

Enable Claude to assist with security log analysis across all major platforms. Claude directly parses and analyzes log samples provided by the user, builds SIEM queries for any platform, creates Sigma rules for portable detection, develops correlation rules, and identifies anomalous patterns in log data.

Activation Triggers

This skill activates when the user asks about:

Parsing Windows Event Logs, Linux syslog, or application logs
Building Splunk SPL, Elastic KQL/EQL, QRadar AQL, or Sentinel KQL queries
Creating Sigma rules for platform-agnostic detection
Detecting anomalies or attack patterns in log data
Building SIEM correlation rules for complex attack scenarios
Converting queries between SIEM platforms
Log source health monitoring and gap analysis
Detecting lateral movement, privilege escalation, or persistence in logs
EVTX analysis or Windows audit log review

Prerequisites

pip install pandas pyyaml python-dateutil

Platform tools:

Splunk — Splunk Web, SPL, and SOAR
Elastic Stack — Kibana, KQL, EQL
Microsoft Sentinel — KQL, Workbooks
IBM QRadar — AQL, Rules
Sigma — Platform-agnostic rule format
python-evtx — Parse Windows .evtx files without Windows

Core Capabilities

1. Log Parsing & Analysis

When the user pastes logs or provides log files:

Claude directly reads and analyzes logs to extract security-relevant events.

Windows Event Log — Critical Event IDs:

Event ID	Log	Description
4624	Security	Successful logon — Logon Type 3 (network) is interesting
4625	Security	Failed logon — track source IP for brute force
4648	Security	Logon with explicit credentials (RunAs)
4688	Security	New process created — needs CommandLine auditing enabled
4698	Security	Scheduled task created
4702	Security	Scheduled task updated
4720	Security	User account created
4728/4732	Security	Member added to security/local group
4768/4769	Security	Kerberos TGT/TGS requested
4776	Security	NTLM authentication
4946	Security	Windows Firewall rule added
5140	Security	Network share accessed
5145	Security	Network share file access
7045	System	New service installed
1102	Security	Audit log cleared
4103/4104	PowerShell	PowerShell module/script block logging

Linux Log Analysis — Key Patterns:

# Failed SSH logins
grep "Failed password" /var/log/auth.log | awk '{print $1,$2,$3,$11}' | sort | uniq -c | sort -rn

# Successful logins after failures (brute force success)
grep "Accepted password\|Accepted publickey" /var/log/auth.log

# Sudo usage
grep "sudo:" /var/log/auth.log | grep -v "session"

# Cron job execution
grep CRON /var/log/syslog

# New user creation
grep "useradd\|usermod" /var/log/auth.log

# Privilege escalation
grep "su\b" /var/log/auth.log

Log parsing script:

python scripts/log_parser.py --input /var/log/auth.log --format json --output parsed.json
python scripts/log_parser.py --input events.evtx --normalize ecs --output normalized.json

2. SIEM Query Library

When the user asks to build detection queries:

Splunk SPL — Attack Pattern Queries

// Brute force attack detection
index=windows EventCode=4625
| bin _time span=5m
| stats count as FailedLogins, values(Account_Name) as Accounts by src_ip, _time
| where FailedLogins > 20
| sort -FailedLogins

// Pass-the-Hash detection (Logon Type 3 with NTLM)
index=windows EventCode=4624 Logon_Type=3 Authentication_Package=NTLM
| where NOT (Account_Name="ANONYMOUS LOGON" OR Account_Name="*$")
| stats count by Account_Name, Workstation_Name, src_ip
| where count > 1

// Lateral movement via PsExec / admin shares
index=windows EventCode=5145
| where (ShareName="\\\\*\\ADMIN$" OR ShareName="\\\\*\\C$") 
    AND RelativeTargetName="*PSEXESVC*"
| table _time, SubjectUserName, IpAddress, ShareName

// PowerShell encoded command execution
index=windows (source="WinEventLog:Microsoft-Windows-PowerShell/Operational" EventCode=4104)
    OR (EventCode=4688 CommandLine="*powershell*")
| search CommandLine IN ("*-EncodedCommand*", "*-enc *", "*-e *", "*-nop*", 
                          "*DownloadString*", "*IEX*", "*Invoke-Expression*")
| table _time, ComputerName, User, CommandLine

// Scheduled task creation for persistence
index=windows EventCode=4698
| rex field=TaskContent "<Command>(?P<command>[^<]+)</Command>"
| where NOT match(command, "(?i)\\\\windows\\\\|\\\\microsoft\\\\|\\\\system32\\\\")
| table _time, ComputerName, SubjectUserName, TaskName, command

// LSASS memory access (credential dumping)
index=sysmon EventCode=10 TargetImage="*lsass.exe"
| where NOT (SourceImage IN 
    ("C:\\Windows\\System32\\*", "C:\\Windows\\SysWOW64\\*",
     "C:\\Program Files\\*", "C:\\Program Files (x86)\\*"))
| table _time, SourceImage, GrantedAccess, CallTrace

// DCSync detection
index=windows EventCode=4662 
    (ObjectType="*domainDNS*" OR ObjectType="*19195a5b-6da0-11d0-afd3-00c04fd930c9*")
    (Properties="*Replicating Directory Changes All*" OR Properties="*1131f6ad*")
| where NOT match(SubjectUserName, "(?i)^.*\$$") 
| table _time, SubjectUserName, SubjectDomainName, Properties

// Kerberoasting detection
index=windows EventCode=4769 Ticket_Encryption_Type=0x17
| where NOT (Account_Name="*$" OR Service_Name IN ("krbtgt", "kadmin/changepw"))
| stats count by Account_Name, Client_Address, Service_Name
| where count > 3

Microsoft Sentinel KQL — Queries

// Impossible Travel (logins from geographically impossible locations)
let TimeDelta = 2h;
SigninLogs
| where ResultType == 0  // Successful logins only
| where TimeGenerated > ago(7d)
| project UserPrincipalName, Location, TimeGenerated, IPAddress
| sort by UserPrincipalName asc, TimeGenerated asc
| serialize
| extend PreviousLogin = prev(TimeGenerated), PreviousLocation = prev(Location)
| where UserPrincipalName == prev(UserPrincipalName)
| extend TimeDiff = TimeGenerated - PreviousLogin
| where TimeDiff < TimeDelta and Location != PreviousLocation
| project UserPrincipalName, Location, PreviousLocation, TimeDiff, IPAddress

// Azure AD privilege escalation
AuditLogs
| where OperationName in ("Add member to role", "Add eligible member to role")
| extend TargetUser = tostring(TargetResources[0].userPrincipalName)
| extend RoleAdded = tostring(TargetResources[0].displayName)  
| where RoleAdded in ("Global Administrator", "Security Administrator", 
                       "Exchange Administrator", "SharePoint Administrator")
| project TimeGenerated, TargetUser, RoleAdded, 
           InitiatedBy=tostring(InitiatedBy.user.userPrincipalName)

// Suspicious PowerShell activity
SecurityEvent
| where EventID == 4104
| where TimeGenerated > ago(24h)
| where ScriptBlockText has_any("IEX", "DownloadString", "EncodedCommand", 
                                  "WebClient", "Invoke-Expression", "bypass", "-nop")
| project TimeGenerated, Computer, Account, ScriptBlockText
| extend RiskScore = case(
    ScriptBlockText has "IEX" and ScriptBlockText has "DownloadString", 10,
    ScriptBlockText has "EncodedCommand", 7,
    ScriptBlockText has "bypass", 5, 3)
| where RiskScore >= 5
| order by RiskScore desc

Elastic EQL — Sequence Detection

// Detect fileless malware execution chain
sequence by host.name with maxspan=5m
  [process where event.type == "start" and
   process.name in ("outlook.exe", "winword.exe", "excel.exe")]
  [process where event.type == "start" and
   process.name in ("powershell.exe", "cmd.exe", "wscript.exe", "cscript.exe")]
  [network where network.direction == "egress" and
   not network.destination.ip in ("127.0.0.0/8", "10.0.0.0/8", "192.168.0.0/16")]

// Ransomware detection: mass file extension changes + shadow copy deletion
sequence by host.name with maxspan=30m
  [file where event.type == "creation" and
   file.extension in ("locked", "encrypted", "crypted", "enc", "readme")]
  [file where event.type == "creation" and
   file.name in ("README.txt", "DECRYPT.txt", "HOW_TO_DECRYPT.txt")]
  [process where event.type == "start" and
   process.command_line : "* delete shadows *"]

3. Anomaly Detection Methodology

When the user asks to detect anomalies in log data:

Statistical Anomaly Detection:

# Claude's approach to analyzing log data for anomalies:
import pandas as pd
from datetime import timedelta

# 1. Volume anomalies
# Calculate rolling average, flag if current > mean + 3*stddev

# 2. Time-based anomalies (off-hours activity)
# Business hours: Mon-Fri 08:00-18:00 local time
# Flag: admin activities on weekends, logins at 03:00 UTC

# 3. Never-before-seen entities
# - New admin account created
# - First-time login from country
# - New process never seen before
# - New domain in DNS queries

# 4. Impossible travel
# Calculate geographic distance / time delta
# Flag if impossible to travel physically in the time window

python scripts/anomaly_detector.py --logs parsed.json --baseline baseline.json --output anomalies.json

Anomaly Categories:

Category	Indicators
Volume spike	10x normal event rate in 5 minutes
Off-hours activity	Admin access at 03:00 local time
New geography	Login from country with no prior history
New process	First-ever execution of binary
Large data transfer	Upload > 10x baseline for this user/system
Silent log source	No events received in 30+ minutes
Authentication pattern	Logon Type 3 from non-admin workstation

4. Sigma Rule Development

When the user asks to create Sigma rules:

title: Credential Dumping via Procdump
id: e5eb5a27-4a98-4c34-8b39-1fbe552d2aa4
status: stable
description: Detects the use of ProcDump to dump LSASS memory for credential theft
author: SOC Analyst
date: 2025/05/28
references:
  - https://attack.mitre.org/techniques/T1003/001/
  - https://docs.microsoft.com/en-us/sysinternals/downloads/procdump
tags:
  - attack.credential_access
  - attack.t1003.001
logsource:
  category: process_creation
  product: windows
detection:
  selection_tool:
    Image|endswith:
      - '\procdump.exe'
      - '\procdump64.exe'
  selection_lsass:
    CommandLine|contains:
      - 'lsass'
      - '-ma 4'      # PID 4 = System, sometimes used
  selection_flags:
    CommandLine|contains|all:
      - '-accepteula'
      - '-ma'
  condition: selection_tool and (selection_lsass or selection_flags)
falsepositives:
  - Legitimate use by administrators for debugging (rare, should be investigated)
level: high

Sigma rule conversion to SIEM platforms:

# Install sigma-cli
pip install sigma-cli

# Convert to Splunk SPL
sigma convert -t splunk -p splunk_windows sigma_rule.yml

# Convert to Elastic KQL
sigma convert -t elasticsearch -p ecs_windows sigma_rule.yml

# Convert to Microsoft Sentinel KQL  
sigma convert -t kusto sigma_rule.yml

5. Correlation Rule Development

When the user asks to create correlation rules for multi-event detection:

## Correlation Rule: Brute Force → Successful Login → Lateral Movement

**Trigger:** 
  Event 1: 4625 (Failed Login) × 20+ in 5 minutes (same source IP)
  THEN Event 2: 4624 (Successful Login) from same source IP within 10 minutes
  THEN Event 3: 5145 (Admin Share Access) from same host within 30 minutes

**Logic:**

Step 1: Bucket failed logins by source IP in 5-minute windows Step 2: If count > 20 → mark IP as "brute f

Voir la source ↗

Métadonnées du fichier

name: Log Analysis & SIEM Integration
description: Security log parsing, anomaly detection, SIEM query building, Sigma rule creation, and correlation rule development across Splunk, Elastic, QRadar, and Microsoft Sentinel
version: 3.0.0
author: Masriyan
tags: [cybersecurity, log-analysis, siem, splunk, elastic, sentinel, sigma, anomaly-detection, correlation]

Voir le texte original

---
name: Log Analysis & SIEM Integration
description: Security log parsing, anomaly detection, SIEM query building, Sigma rule creation, and correlation rule development across Splunk, Elastic, QRadar, and Microsoft Sentinel
version: 3.0.0
author: Masriyan
tags: [cybersecurity, log-analysis, siem, splunk, elastic, sentinel, sigma, anomaly-detection, correlation]
---

# Log Analysis & SIEM Integration

## Purpose

Enable Claude to assist with security log analysis across all major platforms. Claude directly parses and analyzes log samples provided by the user, builds SIEM queries for any platform, creates Sigma rules for portable detection, develops correlation rules, and identifies anomalous patterns in log data.

---

## Activation Triggers

This skill activates when the user asks about:
- Parsing Windows Event Logs, Linux syslog, or application logs
- Building Splunk SPL, Elastic KQL/EQL, QRadar AQL, or Sentinel KQL queries
- Creating Sigma rules for platform-agnostic detection
- Detecting anomalies or attack patterns in log data
- Building SIEM correlation rules for complex attack scenarios
- Converting queries between SIEM platforms
- Log source health monitoring and gap analysis
- Detecting lateral movement, privilege escalation, or persistence in logs
- EVTX analysis or Windows audit log review

---

## Prerequisites

```bash
pip install pandas pyyaml python-dateutil
```

**Platform tools:**
- `Splunk` — Splunk Web, SPL, and SOAR
- `Elastic Stack` — Kibana, KQL, EQL
- `Microsoft Sentinel` — KQL, Workbooks
- `IBM QRadar` — AQL, Rules
- `Sigma` — Platform-agnostic rule format
- `python-evtx` — Parse Windows .evtx files without Windows

---

## Core Capabilities

### 1. Log Parsing & Analysis

**When the user pastes logs or provides log files:**

Claude directly reads and analyzes logs to extract security-relevant events.

**Windows Event Log — Critical Event IDs:**

| Event ID | Log | Description |
|----------|-----|-------------|
| 4624 | Security | Successful logon — Logon Type 3 (network) is interesting |
| 4625 | Security | Failed logon — track source IP for brute force |
| 4648 | Security | Logon with explicit credentials (RunAs) |
| 4688 | Security | New process created — needs CommandLine auditing enabled |
| 4698 | Security | Scheduled task created |
| 4702 | Security | Scheduled task updated |
| 4720 | Security | User account created |
| 4728/4732 | Security | Member added to security/local group |
| 4768/4769 | Security | Kerberos TGT/TGS requested |
| 4776 | Security | NTLM authentication |
| 4946 | Security | Windows Firewall rule added |
| 5140 | Security | Network share accessed |
| 5145 | Security | Network share file access |
| 7045 | System | New service installed |
| 1102 | Security | Audit log cleared |
| 4103/4104 | PowerShell | PowerShell module/script block logging |

**Linux Log Analysis — Key Patterns:**
```bash
# Failed SSH logins
grep "Failed password" /var/log/auth.log | awk '{print $1,$2,$3,$11}' | sort | uniq -c | sort -rn

# Successful logins after failures (brute force success)
grep "Accepted password\|Accepted publickey" /var/log/auth.log

# Sudo usage
grep "sudo:" /var/log/auth.log | grep -v "session"

# Cron job execution
grep CRON /var/log/syslog

# New user creation
grep "useradd\|usermod" /var/log/auth.log

# Privilege escalation
grep "su\b" /var/log/auth.log
```

**Log parsing script:**
```bash
python scripts/log_parser.py --input /var/log/auth.log --format json --output parsed.json
python scripts/log_parser.py --input events.evtx --normalize ecs --output normalized.json
```

### 2. SIEM Query Library

**When the user asks to build detection queries:**

#### Splunk SPL — Attack Pattern Queries

```spl
// Brute force attack detection
index=windows EventCode=4625
| bin _time span=5m
| stats count as FailedLogins, values(Account_Name) as Accounts by src_ip, _time
| where FailedLogins > 20
| sort -FailedLogins

// Pass-the-Hash detection (Logon Type 3 with NTLM)
index=windows EventCode=4624 Logon_Type=3 Authentication_Package=NTLM
| where NOT (Account_Name="ANONYMOUS LOGON" OR Account_Name="*$")
| stats count by Account_Name, Workstation_Name, src_ip
| where count > 1

// Lateral movement via PsExec / admin shares
index=windows EventCode=5145
| where (ShareName="\\\\*\\ADMIN$" OR ShareName="\\\\*\\C$") 
    AND RelativeTargetName="*PSEXESVC*"
| table _time, SubjectUserName, IpAddress, ShareName

// PowerShell encoded command execution
index=windows (source="WinEventLog:Microsoft-Windows-PowerShell/Operational" EventCode=4104)
    OR (EventCode=4688 CommandLine="*powershell*")
| search CommandLine IN ("*-EncodedCommand*", "*-enc *", "*-e *", "*-nop*", 
                          "*DownloadString*", "*IEX*", "*Invoke-Expression*")
| table _time, ComputerName, User, CommandLine

// Scheduled task creation for persistence
index=windows EventCode=4698
| rex field=TaskContent "<Command>(?P<command>[^<]+)</Command>"
| where NOT match(command, "(?i)\\\\windows\\\\|\\\\microsoft\\\\|\\\\system32\\\\")
| table _time, ComputerName, SubjectUserName, TaskName, command

// LSASS memory access (credential dumping)
index=sysmon EventCode=10 TargetImage="*lsass.exe"
| where NOT (SourceImage IN 
    ("C:\\Windows\\System32\\*", "C:\\Windows\\SysWOW64\\*",
     "C:\\Program Files\\*", "C:\\Program Files (x86)\\*"))
| table _time, SourceImage, GrantedAccess, CallTrace

// DCSync detection
index=windows EventCode=4662 
    (ObjectType="*domainDNS*" OR ObjectType="*19195a5b-6da0-11d0-afd3-00c04fd930c9*")
    (Properties="*Replicating Directory Changes All*" OR Properties="*1131f6ad*")
| where NOT match(SubjectUserName, "(?i)^.*\$$") 
| table _time, SubjectUserName, SubjectDomainName, Properties

// Kerberoasting detection
index=windows EventCode=4769 Ticket_Encryption_Type=0x17
| where NOT (Account_Name="*$" OR Service_Name IN ("krbtgt", "kadmin/changepw"))
| stats count by Account_Name, Client_Address, Service_Name
| where count > 3
```

#### Microsoft Sentinel KQL — Queries

```kql
// Impossible Travel (logins from geographically impossible locations)
let TimeDelta = 2h;
SigninLogs
| where ResultType == 0  // Successful logins only
| where TimeGenerated > ago(7d)
| project UserPrincipalName, Location, TimeGenerated, IPAddress
| sort by UserPrincipalName asc, TimeGenerated asc
| serialize
| extend PreviousLogin = prev(TimeGenerated), PreviousLocation = prev(Location)
| where UserPrincipalName == prev(UserPrincipalName)
| extend TimeDiff = TimeGenerated - PreviousLogin
| where TimeDiff < TimeDelta and Location != PreviousLocation
| project UserPrincipalName, Location, PreviousLocation, TimeDiff, IPAddress

// Azure AD privilege escalation
AuditLogs
| where OperationName in ("Add member to role", "Add eligible member to role")
| extend TargetUser = tostring(TargetResources[0].userPrincipalName)
| extend RoleAdded = tostring(TargetResources[0].displayName)  
| where RoleAdded in ("Global Administrator", "Security Administrator", 
                       "Exchange Administrator", "SharePoint Administrator")
| project TimeGenerated, TargetUser, RoleAdded, 
           InitiatedBy=tostring(InitiatedBy.user.userPrincipalName)

// Suspicious PowerShell activity
SecurityEvent
| where EventID == 4104
| where TimeGenerated > ago(24h)
| where ScriptBlockText has_any("IEX", "DownloadString", "EncodedCommand", 
                                  "WebClient", "Invoke-Expression", "bypass", "-nop")
| project TimeGenerated, Computer, Account, ScriptBlockText
| extend RiskScore = case(
    ScriptBlockText has "IEX" and ScriptBlockText has "DownloadString", 10,
    ScriptBlockText has "EncodedCommand", 7,
    ScriptBlockText has "bypass", 5, 3)
| where RiskScore >= 5
| order by RiskScore desc
```

#### Elastic EQL — Sequence Detection

```eql
// Detect fileless malware execution chain
sequence by host.name with maxspan=5m
  [process where event.type == "start" and
   process.name in ("outlook.exe", "winword.exe", "excel.exe")]
  [process where event.type == "start" and
   process.name in ("powershell.exe", "cmd.exe", "wscript.exe", "cscript.exe")]
  [network where network.direction == "egress" and
   not network.destination.ip in ("127.0.0.0/8", "10.0.0.0/8", "192.168.0.0/16")]

// Ransomware detection: mass file extension changes + shadow copy deletion
sequence by host.name with maxspan=30m
  [file where event.type == "creation" and
   file.extension in ("locked", "encrypted", "crypted", "enc", "readme")]
  [file where event.type == "creation" and
   file.name in ("README.txt", "DECRYPT.txt", "HOW_TO_DECRYPT.txt")]
  [process where event.type == "start" and
   process.command_line : "* delete shadows *"]
```

### 3. Anomaly Detection Methodology

**When the user asks to detect anomalies in log data:**

**Statistical Anomaly Detection:**

```python
# Claude's approach to analyzing log data for anomalies:
import pandas as pd
from datetime import timedelta

# 1. Volume anomalies
# Calculate rolling average, flag if current > mean + 3*stddev

# 2. Time-based anomalies (off-hours activity)
# Business hours: Mon-Fri 08:00-18:00 local time
# Flag: admin activities on weekends, logins at 03:00 UTC

# 3. Never-before-seen entities
# - New admin account created
# - First-time login from country
# - New process never seen before
# - New domain in DNS queries

# 4. Impossible travel
# Calculate geographic distance / time delta
# Flag if impossible to travel physically in the time window
```

```bash
python scripts/anomaly_detector.py --logs parsed.json --baseline baseline.json --output anomalies.json
```

**Anomaly Categories:**

| Category | Indicators |
|----------|-----------|
| Volume spike | 10x normal event rate in 5 minutes |
| Off-hours activity | Admin access at 03:00 local time |
| New geography | Login from country with no prior history |
| New process | First-ever execution of binary |
| Large data transfer | Upload > 10x baseline for this user/system |
| Silent log source | No events received in 30+ minutes |
| Authentication pattern | Logon Type 3 from non-admin workstation |

### 4. Sigma Rule Development

**When the user asks to create Sigma rules:**

```yaml
title: Credential Dumping via Procdump
id: e5eb5a27-4a98-4c34-8b39-1fbe552d2aa4
status: stable
description: Detects the use of ProcDump to dump LSASS memory for credential theft
author: SOC Analyst
date: 2025/05/28
references:
  - https://attack.mitre.org/techniques/T1003/001/
  - https://docs.microsoft.com/en-us/sysinternals/downloads/procdump
tags:
  - attack.credential_access
  - attack.t1003.001
logsource:
  category: process_creation
  product: windows
detection:
  selection_tool:
    Image|endswith:
      - '\procdump.exe'
      - '\procdump64.exe'
  selection_lsass:
    CommandLine|contains:
      - 'lsass'
      - '-ma 4'      # PID 4 = System, sometimes used
  selection_flags:
    CommandLine|contains|all:
      - '-accepteula'
      - '-ma'
  condition: selection_tool and (selection_lsass or selection_flags)
falsepositives:
  - Legitimate use by administrators for debugging (rare, should be investigated)
level: high
```

**Sigma rule conversion to SIEM platforms:**
```bash
# Install sigma-cli
pip install sigma-cli

# Convert to Splunk SPL
sigma convert -t splunk -p splunk_windows sigma_rule.yml

# Convert to Elastic KQL
sigma convert -t elasticsearch -p ecs_windows sigma_rule.yml

# Convert to Microsoft Sentinel KQL  
sigma convert -t kusto sigma_rule.yml
```

### 5. Correlation Rule Development

**When the user asks to create correlation rules for multi-event detection:**

```markdown
## Correlation Rule: Brute Force → Successful Login → Lateral Movement

**Trigger:** 
  Event 1: 4625 (Failed Login) × 20+ in 5 minutes (same source IP)
  THEN Event 2: 4624 (Successful Login) from same source IP within 10 minutes
  THEN Event 3: 5145 (Admin Share Access) from same host within 30 minutes

**Logic:**
```
Step 1: Bucket failed logins by source IP in 5-minute windows
Step 2: If count > 20 → mark IP as "brute f

Examiner la source

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Licence: MIT
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Réviser avant installation: Éviter l’installation automatique

Licence: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
The SKILL.md excerpt contains a truncated SPL query example (ends with `| where NOT match(command, "(?i)\\windows\\|\\microsoft`) that is incomplete.
The prerequisites mention `python-evtx` as a platform tool but it is not included in the `pip install` command; it may need separate installation.
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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Dépôt source: Masriyan/Claude-Code-CyberSecurity-Skill
Licence: MIT
Version: 3.0.0
Dernier push GitHub: 3 sept. 2026
Registre mis à jour: 5 sept. 2026
Chemin des instructions: skills/12-log-analysis/SKILL.md @ 504fe672acce

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

74/100

Solide

Confiance

58/100

Do not auto-install

Audit

75/100

Revue nécessaire

Ouvrir l’audit complet Voir le rapport d’évaluation

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
The SKILL.md excerpt contains a truncated SPL query example (ends with `| where NOT match(command, "(?i)\\windows\\|\\microsoft`) that is incomplete.
The prerequisites mention `python-evtx` as a platform tool but it is not included in the `pip install` command; it may need separate installation.
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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Résultats: —

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Pour le créateur

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Créateur: Masriyan
Source: Masriyan/Claude-Code-CyberSecurity-Skill
Indexé par: Index communautaire OpenAgentSkill

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Vue d’ensemble

Log Analysis & SIEM Integration

Purpose

Activation Triggers

Prerequisites

Core Capabilities

1. Log Parsing & Analysis

2. SIEM Query Library

Splunk SPL — Attack Pattern Queries

Microsoft Sentinel KQL — Queries

Elastic EQL — Sequence Detection

3. Anomaly Detection Methodology

4. Sigma Rule Development

5. Correlation Rule Development

Examiner la source

Prix et coûts d’utilisation

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Source et conseils d’utilisation

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