Advanced Machine Learning Model for Anticipating and Preventing Cyber Attacks Using Random Forest (RF)
TL;DR
An Advanced Machine Learning Model for Anticipating and Preventing Cyber Attacks Using Random Forest is presented, which effectively detects cyber threats with high accuracy and reliability, thereby improving threat anticipation and reducing security risks.
Abstract
The rapid advancement of digital technologies, cloud computing, and internet-based services has significantly increased the occurrence of cyber threats and security breaches. Traditional cybersecurity systems primarily rely on signature-based detection techniques, which often fail to identify new and evolving cyberattacks. This limitation creates a need for intelligent and automated solutions capable of detecting malicious activities in real time. To address this challenge, the proposed research presents an Advanced Machine Learning Model for Anticipating and Preventing Cyber Attacks Using Random Forest (RF). The system collects network traffic data, performs preprocessing and feature extraction, and applies the Random Forest algorithm to classify network activities as safe, suspicious, or malicious. In addition, the framework provides real-time monitoring, alert generation, attack classification, and prevention recommendations to enhance cybersecurity management. Experimental results demonstrate that the proposed model effectively detects cyber threats with high accuracy and reliability, thereby improving threat anticipation and reducing security risks. The proposed framework offers a scalable and intelligent solution for strengthening modern cybersecurity infrastructures