WHAT PROGRAMMING LANGUAGES AND TOOLS WOULD BE RECOMMENDED FOR DEVELOPING A CYBERSECURITY VULNERABILITY ASSESSMENT TOOL

There are several programming languages and tools that would be well-suited for developing a cybersecurity vulnerability assessment tool. The key considerations when selecting languages and frameworks include flexibility, extensibility, security features, community support, and interoperability with other systems.

For the primary development language, Python would be an excellent choice. Python has become the de facto standard for security applications due to its extensive ecosystem of libraries, readability, and support for multiple paradigms. Major vulnerability scanning platforms like Nmap and Hydra are implemented in Python, demonstrating its viability for this type of tool. Some key Python libraries that could be leveraged include nmap, Django/Flask for the UI, SQLAlchemy for the database, xmltodict for parsing results, and matplotlib for visualizations.

JavaScript would also be a valid option, enabled by frameworks like Node.js. This could allow a richer front-end experience compared to Python, while still relying on Python in the backend for performance-critical tasks like scanning. Frameworks like Electron could package the application as a desktop program. The asynchronous nature of Node would help make long-running scanning operations more efficient.

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For the main application framework, Django or Flask would be good choices in Python due to their maturity, security features like CSRF protection, and large ecosystem. These provide a solid MVC framework out of the box with tools for user auth, schema migration, and APIs. Alternatively, in JavaScript, frameworks like Express, Next.js and Nest could deliver responsive and secure frontend/backend capabilities.

In addition to the primary languages, other technologies could play supporting roles:

C/C++ – For performance-critical libraries like network packet crafting/parsing. libpcap, DNSEnum, Masscan were written in C.

Go – For high-performance network services within the application. Could offload intensive tasks from the primary lang.

SQL (e.g. PostgreSQL) – To store scanned data, configuration, rules, etc. in a database. Include robust models and migrator.

NoSQL (e.g. MongoDB) – May be useful for certain unstructured data like plugin results.

Docker – Critical for easily deployable, reproducible, and upgradeable application packages.

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Kubernetes – To deploy containerized app at scale across multiple machines.

Prometheus – To collect and store metrics from scanner processes.

Grafana – For visualizing scanning metrics over time (performance, issues found, etc).

On the scanning side, the tool should incorporate existing open-source vulnerability scanning frameworks rather than building custom scanners due to the immense effort required. Frameworks like Nmap, OpenVAS, Nessus and Metasploit provide exhaustive libraries for discovery, banners, OS/service detection, vulnerability testing, and exploitation that have been extensively tested and hardened. The tool can securely invoke these frameworks over APIs or CLI and parse/normalize their output. It can also integrate commercial tools as paid add-ons.

Custom scanners may still be developed as plug-ins for techniques not covered by existing tools, like custom DAST crawlers, specialized configuration analyzers, or dynamic application analysis. The tool should support an extensible plugin architecture allowing third-parties to integrate new analysis modules over a standardized interface. Basic plugins could be developed in the core languages, with more intense ones like fuzzers in C/C++.

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For the interface, a responsive SPA-style Web UI implemented in JavaScript with a REST API backend would provide the most flexible access. It enables a convenient GUI as well as programmatic use. The API design should follow best practices for security, documentation, and versioning. Authentication is crucial, using a mechanism like JSON Web Tokens enforced by the frontend framework. Authorization and activity logging must also be integrated. Regular security testing of the app is critical before deployment.

A combination of Python, JavaScript, C/C++, SQL/NoSQL would likely provide the best balance of capabilities for a full-featured, high-performance, secure and extensible vulnerability assessment tool. By leveraging maturity of established frameworks and libraries, the effort can focus on integration work rather than re-implementing common solutions. With a layered architecture, scalable deployment, and emphasis on testability and open architecture – such a tool could effectively and reliably assess security of a wide range of target environments.

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