An Automated Compliance Framework for Critical Infrastructure Security Through Artificial Intelligence

The project titled "An Automated Compliance Framework for Critical Infrastructure Security Through Artificial Intelligence" aims to develop an intelligent system that ensures the security of critical infrastructure by predicting and recommending compliance actions based on network attack detection. Leveraging machine learning models such as Random Forest, Support Vector Regression (SVR), and Logistic Regression, this framework processes network traffic data to predict potential security threats such as Cross-Site Scripting (XSS), Distributed Denial of Service (DDoS), Man-in-the-Middle (MITM) attacks, SQL Injection, and more. The system integrates a secure web application built with Flask, MySQL, and joblib for model deployment, providing real-time predictions. By utilizing AI and machine learning algorithms, the platform can identify attack types and suggest specific compliance measures to mitigate risks, such as input validation, multi-factor authentication, network traffic filtering, and real-time monitoring. The user interface is designed to allow easy interaction with the system, enabling users to upload network data, select prediction models, and visualize compliance recommendations. The project’s ultimate goal is to automate the compliance and security process, ensuring timely responses to emerging security threats in critical infrastructure.

Keywords:

Automated Compliance, Critical Infrastructure Security, Artificial Intelligence, Network Attack Detection, Machine Learning, Flask, MySQL, Real-time Prediction, Compliance Recommendations, Network Security, Predictive Models, Flask Web Application, Security Framework