Paper Title
Proactive Malware Dectection to Secure Web Data Using Random Forest Algorithm

Abstract
In today’s digital age, ensuring accuracy is crucial. The “PROACTIVE MAWARE DETECTOR” project Addessess this needs by offering a reliable solution for verifying digital content’s authenticity and integrity. This is increasingly important due to rising data manipulation, cyber-attacks, and declining trust in digital communication. The project's main goal is to develop a comprehensive tool that integrates easily into various applications and systems. Utilizing advanced algorithms, the tool verifies data integrity, allowing users to confirm the authenticity of files, documents, and communications. This project's potential applications span industries such as cybersecurity, digital forensics, secure communications, and document authentication. Keywords - Proactive Malware Detector, Data Integrity, Cybersecurity, Secure, Communications, Document Authentication, File Integrity Monitoring (FIM), Malware Detection, Random Forest Model, Data Preprocessing, Feature Extraction, Real-time Monitoring, Security Threats