List of project ideas for contributors applying to the Google Summer of Code program in 2024.
Please always refer to the official timeline.
Please always refer to the main page of this repository
You can also propose your own.
Mentors: Matteo Lodi, Daniele Rosetti
Project URL: All IntelOwl Organization Projects are impacted
Project hours: 175
Skills required: Python (Django) and willingness to explore and adapt to new frameworks and open source projects
Difficulty: Low
Description:
Right now we are not satisfied of how we manage our documentation and how we make it available.
The project aims to create a new repository dedicated to the documentation, move all the documentation of all our projects there and build a new documentation site by leveraging Github Pages and MkDocs.
More information in this Github Issue
The candidate would have the chance to try some popular tools and to solve a big common problem that a lot of other Open Source projects have. The ideal candidate is proactive in reading documentation of new tools and excited in trying them to solve our problem.
Mentors: Matteo Lodi, Daniele Rosetti, Simone Berni
Project URL: IntelOwl
Project hours: 175
Skills required: Docker, Python (Django), JavaScript (React.js), Object-Oriented Programming
Difficulty: Medium
Description:
Right now there are many possible types of plugins in IntelOwl.
This project aims to add a new plugin type to the already existing ones in IntelOwl:
- The "Scanner" type would be a subtype of the “Analyzers” ones with special configuration. In that way, IntelOwl could be used not only for classic data enrichment with external services but as either a vulnerability scanner or a scraper too. Refer to the Github Issue for more details
Like we have similarly done with other GSoC projects in the past that added new plugin types, we expect the contributor to add the most important new scanners (like this) to IntelOwl once he finishes building the framework to provide a base of tools which can be used by the users.
The candidate would have the chance to work through all the application stack (backend and frontend). The ideal candidate for this project is someone who is familiar with how IntelOwl works and its core concepts.
Mentors: Matteo Lodi, Daniele Rosetti, Simone Berni
Project URL: IntelOwl
Project hours: 175
Skills required: Docker, Python (Django), Object-Oriented Programming
Difficulty: Low
Description:
Right now we have a lot of Analyzers implemented in IntelOwl.
But they are not enough! They are the core part of the application so we want to add even more of them!!!! :)
This project aims to increment the number of available Analyzers. We have about 50 different Analyzers that has been requested by the community members in Github and are still not implemented. We obviously do not ask to implement all of them but a reasonable amount of them based on the available time and the efforts required for each of them.
Adding a new Analyzer to the framework is one of the easiest things that can be done in this project. Once you get used to it, adding more of them is even easier!
The ideal candidate for this project is someone who understand how IntelOwl's framework works and already tried to implement an Analyzer.
Mentors: Hugo Gascon, Matteo Lodi, Daniele Rosetti, Simone Berni
Project URL: IntelOwl
Project hours: 350
Skills required: Python, basic knowledge of the RAG architecture and its necessary libraries (e.g. langchain, chromadb, etc.) and willingness to explore and adapt to new frameworks and open source projects
Difficulty: Medium
Description:
- The proposed Google Summer of Code project aims to integrate a cutting-edge, self-deployed LLM-based chatbot into IntelOwl, enhancing user interaction with collected threat intelligence.
- Leveraging Python libraries like LangChain and ChainLit, the project envisions building an intuitive interface that empowers analysts to pose natural language queries about threat data, fostering a more user-friendly and efficient investigative process (e.g. "In what campaigns have you seen this IOC?")
- The chatbot's capabilities will extend beyond basic queries, seamlessly interfacing with IntelOwl's enrichment modules when deeper investigation is required, providing a comprehensive and interactive experience for analysts.
- By harnessing the power of LLM technology, the chatbot will not only streamline communication between analysts and the IntelOwl platform but also adapt to evolving user needs, contributing to a more dynamic and responsive threat intelligence environment.
- This project aligns with the overarching goal of making threat analysis more accessible and efficient, offering analysts a powerful tool that combines the strengths of natural language understanding, self-deployment, and seamless integration with IntelOwl's