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Log Analyzer

A Python-based CLI security tool for analyzing log files, detecting suspicious events, and exporting structured results for further investigation.

This project was built to demonstrate practical Cyber Security, Detection Engineering, and Python scripting skills in a realistic and useful way. The tool can detect suspicious patterns such as failed logins, unauthorized access, SQL injection attempts, XSS payloads, path traversal attempts, server errors, and repeated activity from the same source IP.

The purpose of this project is to simulate a simplified log analysis workflow similar to what is used in real-world security monitoring environments. The tool runs completely in the terminal and is designed to later support a separate SIEM Mini Dashboard project by exporting results as JSON or CSV.

Table of Contents​

Project Overview​

This repository contains a Python script called log_analyzer.py that analyzes plain text log files from the command line.

The tool reads a selected log file line by line, extracts useful information such as timestamps and source IP addresses, and checks every line against a set of detection rules. If suspicious activity is found, the script classifies the event, assigns a severity level, and includes it in the final analysis report.

The tool can detect several common security-relevant patterns, including authentication failures, forbidden access attempts, injection payloads, server errors, and repeated failed login attempts from the same IP address.

In addition to terminal output, the tool can also generate:

  • a JSON report for structured machine-readable output
  • a CSV report for spreadsheet-style review or later processing
  • a clear CLI summary for quick manual analysis

Why This Project Matters​

Log analysis is an important part of Cyber Security because logs often contain the first signs of suspicious or malicious activity.

A security analyst can use logs to identify:

  • failed login attempts
  • brute force indicators
  • unauthorized access attempts
  • web attack payloads
  • server-side errors
  • suspicious source IP addresses
  • repeated abnormal behavior

This project demonstrates how raw log data can be transformed into structured security events. Although this tool is a simplified standalone Python implementation, it follows the same basic idea used in Security Operations Center workflows and SIEM systems.

Features​

FeatureDescription
Log file analysisReads and analyzes plain text log files
Failed login detectionDetects failed authentication attempts
Brute force indicatorFlags repeated failed logins from the same IP
Unauthorized access detectionDetects forbidden or denied access attempts
SQL injection pattern detectionDetects common SQL injection indicators
XSS pattern detectionDetects common cross-site scripting payloads
Path traversal detectionDetects suspicious file path traversal attempts
Server error detectionDetects HTTP 500 errors and traceback indicators
Severity classificationClassifies events as low, medium, high, or critical
Top suspicious IPsShows the most active suspicious IP addresses
JSON exportExports full analysis results as JSON
CSV exportExports detected events as CSV
Colored CLI outputImproves readability in the terminal

Tech Stack​

ComponentPurpose
Python 3Main programming language
argparseCLI argument parsing
reRegex-based detection rules
jsonJSON report export
csvCSV event export
collections.CounterCounting event types, severities, and IPs
dataclassesStructured event and rule objects
datetimeAnalysis timestamp generation
pathlibFile and path handling
coloramaColored terminal output

How It Works​

1. Log File Input​

The user provides a log file with the --file option. The tool checks if the file exists and then reads it line by line.

2. Pattern Detection​

Each line is checked against multiple detection rules. These rules use regular expressions and keywords to identify suspicious patterns such as failed logins, SQL injection payloads, XSS payloads, path traversal strings, and server errors.

3. Severity Classification​

Every detected event receives a severity level:

  • low
  • medium
  • high
  • critical

This makes the output easier to understand and helps prioritize suspicious findings.

4. Brute Force Detection​

The tool counts failed login attempts per source IP address. If one IP reaches the configured threshold, the tool creates an additional Brute Force Indicator event.

By default, the threshold is set to 5.

5. Exporting Results​

The tool can export the analysis results as:

  • JSON for structured data and later dashboard usage
  • CSV for spreadsheet-style analysis
  • terminal output for quick manual review

Prerequisites​

Before using the tool, make sure the following software is available:

  • Python 3.10 or newer
  • pip
  • a terminal environment such as macOS Terminal, Linux shell, or VS Code integrated terminal

Optional but recommended:

  • a virtual environment
  • colorama for colored CLI output

Quickstart​

Clone the repository:

git clone <your-repository-url>

Move into the project directory:

cd log-analyzer

Create a virtual environment:

python3 -m venv venv

Activate the virtual environment:

source venv/bin/activate

Install the dependency:

pip install colorama

Run the analyzer:

python3 log_analyzer.py --file sample_logs/sample.log

Export JSON and CSV reports:

python3 log_analyzer.py --file sample_logs/sample.log --json --csv

How to Run the Tool​

1. Create a Virtual Environment​

python3 -m venv venv

2. Install Dependencies​

Activate the environment:

source venv/bin/activate

Install colorama:

pip install colorama

3. Create a Sample Log File​

Create the sample log directory:

mkdir sample_logs

Create the sample log file:

touch sample_logs/sample.log

Add test log entries:

2026-04-27 10:15:01 INFO Server started successfully from 192.168.1.10
2026-04-27 10:16:12 WARNING Disk usage is above 80%
2026-04-27 10:17:33 ERROR Internal server error from 192.168.1.20 status 500
2026-04-27 10:18:44 Failed login for user admin from 203.0.113.10
2026-04-27 10:18:49 Failed login for user admin from 203.0.113.10
2026-04-27 10:18:55 Failed login for user admin from 203.0.113.10
2026-04-27 10:19:01 Failed login for user admin from 203.0.113.10
2026-04-27 10:19:07 Failed login for user admin from 203.0.113.10
2026-04-27 10:20:10 Unauthorized access attempt from 198.51.100.23 status 403
2026-04-27 10:21:11 GET /login?id=1 OR 1=1 from 198.51.100.55
2026-04-27 10:22:12 GET /search?q=<script>alert(1)</script> from 198.51.100.77
2026-04-27 10:23:13 GET /download?file=../../etc/passwd from 192.0.2.44

4. Run the Basic Analysis​

python3 log_analyzer.py --file sample_logs/sample.log

The tool should display a terminal report with:

  • processed log lines
  • detected suspicious events
  • severity breakdown
  • top event types
  • top suspicious IP addresses
  • recurring error messages

5. Export JSON Results​

python3 log_analyzer.py --file sample_logs/sample.log --json

This creates a JSON report in the output directory.

Expected output file:

output/sample_analysis.json

6. Export CSV Results​

python3 log_analyzer.py --file sample_logs/sample.log --csv

This creates a CSV report in the output directory.

Expected output file:

output/sample_events.csv

7. Export JSON and CSV Together​

python3 log_analyzer.py --file sample_logs/sample.log --json --csv

This creates both export files at the same time.

8. Change the Brute Force Threshold​

The default threshold for brute force detection is 5.

Use a stricter threshold:

python3 log_analyzer.py --file sample_logs/sample.log --threshold 10

Use a more sensitive threshold:

python3 log_analyzer.py --file sample_logs/sample.log --threshold 3

Usage​

Analyze a Log File​

python3 log_analyzer.py --file sample_logs/sample.log

Export JSON Output​

python3 log_analyzer.py --file sample_logs/sample.log --json

Export CSV Output​

python3 log_analyzer.py --file sample_logs/sample.log --csv

Use a Custom Output Directory​

python3 log_analyzer.py --file sample_logs/sample.log --json --csv --output-dir reports

Change the Brute Force Threshold​

python3 log_analyzer.py --file sample_logs/sample.log --threshold 3

Limit Top Results​

python3 log_analyzer.py --file sample_logs/sample.log --top 3

Reports​

The tool can produce the following outputs:

Report TypePurpose
Terminal outputQuick analysis directly in the console
JSON reportStructured export for later processing or dashboard integration
CSV reportSpreadsheet-friendly list of detected events

The JSON export is especially useful for a future SIEM-style dashboard, because it contains structured event data that can be parsed and visualized.

Screenshots​

The following screenshots can be added to document the tool and demonstrate its functionality.

1. Basic Log Analysis​

Shows the basic CLI command and terminal output after analyzing a sample log file.

Basic Log Analysis

2. JSON Output​

Shows the generated JSON analysis file after running the tool with the --json option.

JSON Output

3. CSV Output​

Shows the generated CSV event file after running the tool with the --csv option.

CSV Output

What Was Implemented and Why​

CLI-based workflow​

Implemented so the tool can be used directly from the terminal without a graphical interface.

Regex-based detection rules​

Implemented to detect suspicious log entries using clear and understandable patterns.

IP address extraction​

Implemented to identify source IP addresses and highlight repeated suspicious activity.

Timestamp extraction​

Implemented to preserve time information from matching log entries where possible.

Severity classification​

Implemented to make findings easier to prioritize.

Brute force indicator​

Implemented to detect repeated failed login attempts from the same IP address.

JSON export​

Implemented so the results can later be consumed by a SIEM-style dashboard or another analysis tool.

CSV export​

Implemented to provide a simple structured format for review, documentation, or spreadsheet analysis.

Colored terminal output​

Implemented to improve readability and make important findings easier to notice.

Security Relevance​

This project demonstrates important security concepts such as:

  • log analysis
  • detection engineering
  • event classification
  • brute force detection
  • suspicious IP identification
  • regex-based security detection
  • structured security reporting
  • preparation of data for SIEM-style visualization

Possible real-world use cases include:

  • reviewing authentication logs
  • identifying repeated failed login attempts
  • detecting suspicious web requests
  • finding possible injection attempts
  • analyzing server error patterns
  • preparing log data for dashboards or incident reports

Possible Improvements​

Possible future improvements for this project include:

  • support for multiple log formats
  • Apache and Nginx log parsing profiles
  • SSH authentication log parsing
  • Windows event log import
  • custom user-defined detection rules
  • YAML-based rule configuration
  • HTML report generation
  • alert scoring system
  • timeline visualization
  • integration with the future SIEM Mini Dashboard
  • packaging the tool as an installable CLI utility