AI Analysis
Final verdict: SAFE
The package PyNomaly v0.4.0 is assessed as safe with a low risk score due to the absence of network calls, shell executions, obfuscations, and credential risks. The metadata shows minor concerns but does not indicate any malicious activities.
- No network calls or shell executions detected
- Low obfuscation and credential risk
Per-check LLM notes
- Network: No network calls detected, which is normal for a package focused on anomaly detection in datasets.
- Shell: No shell execution patterns detected, aligning with expectations for a data analysis package.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows some low-effort signs but lacks clear red flags.
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: valentino.io
Suspicious Page Links
score 10.0
Found 6 suspicious link(s) on the package page
Non-HTTPS external link: http://joss.theoj.org/papers/f4d2cfe680768526da7c1f6a2c103266/status.svgNon-HTTPS external link: http://joss.theoj.org/papers/f4d2cfe680768526da7c1f6a2c103266Non-HTTPS external link: http://www.dbs.ifi.lmu.de/Publikationen/Papers/LoOP1649.pdfNon-HTTPS external link: http://www.tandfonline.com/doi/abs/10.1080/23742917.2016.1226651?journalCode=tseNon-HTTPS external link: http://semver.org/Non-HTTPS external link: http://www.dbs.ifi.lmu.de/Publikationen/Papers/LOF.pdf
Git Repository History
Repository vc1492a/PyNomaly appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author "Valentino Constantinou" 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 PyNomaly
Create a web-based anomaly detection system using Python and the PyNomaly package. This mini-application will allow users to upload datasets and identify potential outliers within the data using the LoOP (Local Outlier Probabilities) algorithm implemented in PyNomaly. The application should have the following features: 1. **User Interface**: Develop a simple, user-friendly interface where users can upload CSV files containing their dataset. 2. **Data Preprocessing**: Implement basic data preprocessing steps such as handling missing values, converting categorical data into numerical data if necessary, and scaling the data appropriately for the LoOP algorithm. 3. **Outlier Detection**: Utilize the PyNomaly package to calculate the outlier scores for each data point. Ensure that the scores are normalized between 0 and 1, where 0 represents a normal data point and 1 represents a highly anomalous data point. 4. **Visualization**: Provide visualizations of the data before and after applying the outlier detection algorithm. Include scatter plots or histograms to show the distribution of the outlier scores and highlight the identified outliers. 5. **Results Display**: Display the top N most anomalous data points along with their corresponding outlier scores. Allow users to specify the number of top anomalies they want to see. 6. **Documentation**: Write comprehensive documentation explaining how the application works, how to use it, and what each part of the code does. 7. **Deployment**: Prepare the application for deployment on a cloud platform like Heroku or AWS, ensuring that it can handle multiple concurrent users and efficiently process large datasets. This project aims to demonstrate the practical application of PyNomaly in identifying anomalies within datasets, which could be useful in various fields such as finance, cybersecurity, and healthcare.