Applications of Deep Learning Approaches to Detect Advanced Cyber Attacks

Abstract

The number and sophistication of cyber attacks have grown, making it tougher to detect and prevent them using traditional security technologies. Improving cyber threat identification and response has been greatly enhanced by deep learning, a subset of machine learning. Learn how to spot advanced cyberattacks with the help of Deep Learning algorithms in this article. The proposed approach collects, categorises, and arranges network traffic data using convolutional neural networks (CNNs) and intermittent neural networks (RNNs). Combining the RNN with the CNN allows us to capture temporal dependencies and derive spatial properties from the network data. In order to find the most important qualities for classification, the proposed method also incorporates a feature selection stage. To demonstrate that the proposed system outperforms signature-based and example AI systems in terms of exactness, accuracy, review, and F1-score, we conduct an exhibition survey using various datasets. An effective tool for improving cyber defences, the proposed method can detect zero-day and previously unknown attacks.