anomalog

v0.5.0 safe
4.0
Medium Risk

Reproducible log anomaly detection pipelines, from raw logs to deterministic, template-mapped sequences

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks associated with network, shell execution, obfuscation, and credential handling. However, it exhibits low activity and maintenance effort, which slightly raises concerns.

  • Low network and shell execution risk
  • Signs of low activity and maintenance effort
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network interaction for its functionality.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious shell command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of low activity and maintenance effort, but there are no clear indicators of malicious intent.

📦 Package Quality Overall: Low (4.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://harens.github.io/AnomaLog/
  • Detailed PyPI description (7624 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 199 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in harens/AnomaLog
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: imperial.ac.uk>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "Haren Samarasinghe" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with anomalog 
Create a fully functional mini-application using the Python package 'anomalog' which specializes in reproducible log anomaly detection. Your application will be designed to monitor system logs in real-time, identify anomalies, and provide actionable insights for system administrators. Here’s a detailed plan on how to proceed:

1. **Setup Environment**: Start by setting up a virtual environment and installing necessary packages including 'anomalog'. Additionally, include other dependencies like pandas for data manipulation and matplotlib for visualization.

2. **Data Ingestion**: Design a component that continuously ingests log files from a specified directory or URL. This could be logs from web servers, application servers, or any other sources of interest.

3. **Preprocessing**: Utilize 'anomalog' to preprocess the raw log data into a structured format suitable for analysis. This involves parsing logs into meaningful fields such as timestamp, log level, message content, etc.

4. **Anomaly Detection**: Implement an anomaly detection pipeline using 'anomalog'. This should include steps like normalizing the data, identifying patterns, and detecting deviations from these patterns that signify potential issues.

5. **Visualization**: Integrate a visualization module that uses matplotlib to display trends and anomalies in the logs over time. This should help in quickly spotting unusual activities.

6. **Alerting Mechanism**: Develop a feature where users can set thresholds for anomalies. If detected anomalies exceed these thresholds, the application should send alerts via email or SMS.

7. **Reporting**: Finally, create a reporting tool that generates periodic reports summarizing the health of the monitored systems based on the detected anomalies and trends observed.

Throughout the development process, ensure that your application is modular and well-documented. Use comments and docstrings to explain key functionalities and how they relate to the 'anomalog' package.

💬 Discussion Feed

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