logsentinelai 0.7.0

AI-Powered Log Analyzer - Leverages LLM to analyze log files and detect security events

LogSentinelAI — Declarative LLM-Based Analyzer for Security Events, System Errors, and Anomalies

LogSentinelAI leverages LLM with Declarative Extraction to analyze security events, anomalies, and errors from various logs including Apache, Linux, and converts them into structured data that can be visualized with Elasticsearch/Kibana. Simply declare your desired result structure as a Pydantic class, and the AI automatically analyzes logs to return JSON matching that schema. No complex parsing is required.

System Architecture

Key Features

⚡️ Declarative Extraction

In each analyzer script, simply declare the desired result structure as a Pydantic class, and the LLM will automatically analyze logs and return results as JSON matching that schema. No complex parsing or post-processing—just declare what you want, and the AI handles the rest. This approach enables developers to focus on "what to extract" declaratively, while the LLM takes care of "how to extract"—a modern paradigm for information extraction.

# Example: Just declare the result structure you want in your HTTP Access log analyzer
from pydantic import BaseModel

class MyAccessLogResult(BaseModel):
    ip: str
    url: str
    is_attack: bool

# By defining only the result structure (Pydantic class) like above,
# the LLM automatically analyzes each log and returns JSON like this:
# {
#   "ip": "192.168.0.1",
#   "url": "/admin.php",
#   "is_attack": true
# }

AI-powered Analysis

• Declarative Extraction: Just declare your desired result structure (Pydantic class) and the LLM analyzes logs automatically
• LLM Providers: OpenAI API, Ollama, vLLM
• Supported Log Types: HTTP Access, Apache Error, Linux System
• Threat Detection: SQL Injection, XSS, Brute Force, Network Anomaly Detection
• Output: Structured JSON validated by Pydantic
• Just define a Pydantic class and the LLM generates results in that structure automatically
• Adaptive Sensitivity: Detection sensitivity auto-adjusted by LLM model and log type prompt

Processing Modes

• Batch: Bulk analysis of historical logs
• Real-time: Sampling-based live monitoring
• Access Methods: Local files, SSH remote

Data Enrichment

• GeoIP: MaxMind GeoLite2 City lookup (including coordinates, Kibana geo_point support)
• Statistics: IP counts, response codes, various metrics
• Multi-language Support: Configurable result language (default: Korean)

Enterprise Integration

• Storage: Elasticsearch (ILM policy support)
• Visualization: Kibana dashboard
• Deployment: Docker containers

Dashboard Example

JSON Output Example

CLI Command Mapping

# CLI commands mapped to analyzer scripts:
logsentinelai-httpd-access   → analyzers/httpd_access.py
logsentinelai-httpd-server   → analyzers/httpd_server.py  
logsentinelai-linux-system   → analyzers/linux_system.py
logsentinelai-geoip-download → utils/geoip_downloader.py

Sample Log Preview

HTTP Access Log

54.36.149.41 - - [22/Jan/2019:03:56:14 +0330] "GET /filter/27|13%20%D9%85%DA%AF%D8%A7%D9%BE%DB%8C%DA%A9%D8%B3%D9%84,27|%DA%A9%D9%85%D8%AA%D8%B1%20%D8%A7%D8%B2%205%20%D9%85%DA%AF%D8%A7%D9%BE%DB%8C%DA%A9%D8%B3%D9%84,p53 HTTP/1.1" 200 30577 "-" "Mozilla/5.0 (compatible; AhrefsBot/6.1; +http://ahrefs.com/robot/)" "-"
31.56.96.51 - - [22/Jan/2019:03:56:16 +0330] "GET /image/60844/productModel/200x200 HTTP/1.1" 200 5667 "https://www.zanbil.ir/m/filter/b113" "Mozilla/5.0 (Linux; Android 6.0; ALE-L21 Build/HuaweiALE-L21) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.158 Mobile Safari/537.36" "-"
31.56.96.51 - - [22/Jan/2019:03:56:16 +0330] "GET /image/61474/productModel/200x200 HTTP/1.1" 200 5379 "https://www.zanbil.ir/m/filter/b113" "Mozilla/5.0 (Linux; Android 6.0; ALE-L21 Build/HuaweiALE-L21) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.158 Mobile Safari/537.36" "-"
40.77.167.129 - - [22/Jan/2019:03:56:17 +0330] "GET /image/14925/productModel/100x100 HTTP/1.1" 200 1696 "-" "Mozilla/5.0 (compatible; bingbot/2.0; +http://www.bing.com/bingbot.htm)" "-"
91.99.72.15 - - [22/Jan/2019:03:56:17 +0330] "GET /product/31893/62100/%D8%B3%D8%B4%D9%88%D8%A7%D8%B1-%D8%AE%D8%A7%D9%86%DA%AF%DB%8C-%D9%BE%D8%B1%D9%86%D8%B3%D9%84%DB%8C-%D9%85%D8%AF%D9%84-PR257AT HTTP/1.1" 200 41483 "-" "Mozilla/5.0 (Windows NT 6.2; Win64; x64; rv:16.0)Gecko/16.0 Firefox/16.0" "-"
40.77.167.129 - - [22/Jan/2019:03:56:17 +0330] "GET /image/23488/productModel/150x150 HTTP/1.1" 200 2654 "-" "Mozilla/5.0 (compatible; bingbot/2.0; +http://www.bing.com/bingbot.htm)" "-"
40.77.167.129 - - [22/Jan/2019:03:56:18 +0330] "GET /image/45437/productModel/150x150 HTTP/1.1" 200 3688 "-" "Mozilla/5.0 (compatible; bingbot/2.0; +http://www.bing.com/bingbot.htm)" "-"
40.77.167.129 - - [22/Jan/2019:03:56:18 +0330] "GET /image/576/article/100x100 HTTP/1.1" 200 14776 "-" "Mozilla/5.0 (compatible; bingbot/2.0; +http://www.bing.com/bingbot.htm)" "-"
66.249.66.194 - - [22/Jan/2019:03:56:18 +0330] "GET /filter/b41,b665,c150%7C%D8%A8%D8%AE%D8%A7%D8%B1%D9%BE%D8%B2,p56 HTTP/1.1" 200 34277 "-" "Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)" "-"
40.77.167.129 - - [22/Jan/2019:03:56:18 +0330] "GET /image/57710/productModel/100x100 HTTP/1.1" 200 1695 "-" "Mozilla/5.0 (compatible; bingbot/2.0; +http://www.bing.com/bingbot.htm)" "-"

Apache Server Log

[Thu Jun 09 06:07:04 2005] [notice] LDAP: Built with OpenLDAP LDAP SDK
[Thu Jun 09 06:07:04 2005] [notice] LDAP: SSL support unavailable
[Thu Jun 09 06:07:04 2005] [notice] suEXEC mechanism enabled (wrapper: /usr/sbin/suexec)
[Thu Jun 09 06:07:05 2005] [notice] Digest: generating secret for digest authentication ...
[Thu Jun 09 06:07:05 2005] [notice] Digest: done
[Thu Jun 09 06:07:05 2005] [notice] LDAP: Built with OpenLDAP LDAP SDK
[Thu Jun 09 06:07:05 2005] [notice] LDAP: SSL support unavailable
[Thu Jun 09 06:07:05 2005] [error] env.createBean2(): Factory error creating channel.jni:jni ( channel.jni, jni)
[Thu Jun 09 06:07:05 2005] [error] config.update(): Can't create channel.jni:jni
[Thu Jun 09 06:07:05 2005] [error] env.createBean2(): Factory error creating vm: ( vm, )

Linux System Log

Jun 14 15:16:01 combo sshd(pam_unix)[19939]: authentication failure; logname= uid=0 euid=0 tty=NODEVssh ruser= rhost=218.188.2.4 
Jun 14 15:16:02 combo sshd(pam_unix)[19937]: check pass; user unknown
Jun 14 15:16:02 combo sshd(pam_unix)[19937]: authentication failure; logname= uid=0 euid=0 tty=NODEVssh ruser= rhost=218.188.2.4 
Jun 15 02:04:59 combo sshd(pam_unix)[20882]: authentication failure; logname= uid=0 euid=0 tty=NODEVssh ruser= rhost=220-135-151-1.hinet-ip.hinet.net  user=root
Jun 15 02:04:59 combo sshd(pam_unix)[20884]: authentication failure; logname= uid=0 euid=0 tty=NODEVssh ruser= rhost=220-135-151-1.hinet-ip.hinet.net  user=root
Jun 15 02:04:59 combo sshd(pam_unix)[20883]: authentication failure; logname= uid=0 euid=0 tty=NODEVssh ruser= rhost=220-135-151-1.hinet-ip.hinet.net  user=root
Jun 15 02:04:59 combo sshd(pam_unix)[20885]: authentication failure; logname= uid=0 euid=0 tty=NODEVssh ruser= rhost=220-135-151-1.hinet-ip.hinet.net  user=root
Jun 15 02:04:59 combo sshd(pam_unix)[20886]: authentication failure; logname= uid=0 euid=0 tty=NODEVssh ruser= rhost=220-135-151-1.hinet-ip.hinet.net  user=root
Jun 15 02:04:59 combo sshd(pam_unix)[20892]: authentication failure; logname= uid=0 euid=0 tty=NODEVssh ruser= rhost=220-135-151-1.hinet-ip.hinet.net  user=root
Jun 15 02:04:59 combo sshd(pam_unix)[20893]: authentication failure; logname= uid=0 euid=0 tty=NODEVssh ruser= rhost=220-135-151-1.hinet-ip.hinet.net  user=root

Installation Guide

For installation, environment setup, CLI usage, Elasticsearch/Kibana integration, and all practical guides for LogSentinelAI, please refer to the installation documentation below.

Go to Installation and Usage Guide: INSTALL.en.md

⚡️ For additional inquiries, please use GitHub Issues/Discussions!

Acknowledgments

We would like to express our sincere gratitude to the following projects and communities that provided inspiration, guidance, and foundational technologies for LogSentinelAI:

Core Technologies & Frameworks

• Outlines - Structured LLM output generation framework that powers our reliable AI analysis
• dottxt-ai Demos - Excellent log analysis examples and implementation patterns
• Docker ELK Stack - Comprehensive Elasticsearch, Logstash, and Kibana Docker setup

LLM Infrastructure & Deployment

• vLLM - High-performance LLM inference engine for GPU-accelerated local deployment
• Ollama - Simplified local LLM deployment and management platform

Open Source Community

We are deeply grateful to the broader open source community and the countless projects that have contributed to making AI-powered log analysis accessible and practical. This project stands on the shoulders of many innovative open source initiatives that continue to push the boundaries of what's possible.