End-to-End Network Security Solutions using Deep Learning Algorithms

In recent times, network security threats have evolved, demanding sophisticated solutions. This research introduces an innovative “End-to-End Network Security Solution” harnessing the power of deep learning algorithms. The study integrates three foundational deep learning algorithms: Convolutional Neural Networks (CNNs) for intrusion detection, Long Short-Term Memory (LSTM) networks for behavioral analysis, and Generative Adversarial Networks (GANs) for anomaly detection. The research commences with data preprocessing, followed by the application of CNNs to classify network packets as normal or potentially malicious. Further, LSTM networks delve into the temporal behavior of network traffic, flagging deviations from established patterns. Lastly, GANs play a crucial role in identifying subtle anomalies by understanding the intrinsic data distribution of normal network traffic and contrasting it against synthesized data. Comprehensive evaluations showcase the proposed method's efficacy in comparison to existing approaches, making it a promising solution for modern-day network security challenges.