Machine Learning with Deep Learning Approach for Cyber Security Threats Prevention Model

Abstract

The need for effective cybersecurity solutions has never been greater than in today's world of constant digitalization and interconnection. This study explores the use of deep learning to the creation of a model for preventing cyber security threats using machine learning. The model is meant to successfully identify and prevent cyber-attacks by analysing network traffic and system behaviour using a convolutional neural network (CNN) linked with variationalautoencoders (VAEs). This study's findings illustrate the model's competency in threat recognition with a high degree of accuracy, precision, and recall. Actively monitoring and evaluating network data, providing automatic threat identification and response, further demonstrates its usefulness when used in the real world. This work sets the path for future developments in quantum-resistant encryption, adversarial machine learning studies, deep learning architectures, autonomous cybersecurity systems, explainable AI, and global threat intelligence cooperation. These new approaches have great potential for paving the way towards a more secure and resilient cyber future.