Closed Varunshiyam closed 1 week ago
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| Name | Status | Preview | Comments | Updated (UTC) |
|---|---|---|---|---|
| ml-nexus | ✅ Ready (Inspect) | Visit Preview | 💬 Add feedback | Nov 9, 2024 0:44am |
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github-actions[bot]
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1 week ago 🎉🎉 Thank you for your contribution! Your PR #850 has been merged! 🎉🎉
Related Issues or bug
With the constantly evolving landscape of cyber threats, maintaining security in web and mobile applications is challenging. Attack patterns continuously change, making it essential to develop adaptable and intelligent security systems. This project aims to create a machine learning model that can assess whether incoming network requests are safe or malicious. By identifying malicious requests, this model assists in blocking threats, protecting applications from runtime attacks such as those listed in the OWASP Top 10.
Fixes: #847
Proposed Changes
This project focuses on the development of a machine learning model aimed at enhancing cybersecurity by detecting potentially malicious requests in network traffic. Using a dataset containing information about requests and their classifications as safe or unsafe, the model leverages natural language processing (NLP) techniques on payload data to differentiate between benign and malicious inputs effectively. The task involves training, testing, and evaluating various models to achieve a high degree of accuracy in flagging requests that may represent security threats.