Cyber Hacking Breaches Prediction Using Machine Learning

With the growing number of internet-connected devices, cyber-attacks have become more frequent and sophisticated, posing serious threats to data security and organizational integrity. Traditional security systems are limited in detecting new and complex attacks. This project proposes a machine learning-based approach using a Multilayer Perceptron (MLP) model to predict and classify cyber-attacks. The MLP is trained on labeled cybersecurity data to detect threats such as malware, phishing, data leaks, and SQL injections. Through advanced data preprocessing and pattern recognition, the system offers scalable, automated, and real-time threat detection. This approach enhances cybersecurity by enabling proactive and intelligent decision-making.

Keywords: Cybersecurity, Machine Learning, Multilayer Perceptron (MLP), Threat Detection, Phishing, Malware, Data Breach, SQL Injection, Pattern Recognition, Network Security.