Deep Learning Models for Predictive Cybersecurity: Enhancing Threat Detection and Response in Digital Infrastructures
Published 08-11-2023
Keywords
- Deep Learning,
- Predictive Cybersecurity,
- Threat Detection,
- Convolutional Neural Networks,
- Visual Data Analysis
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
In an era where digital infrastructures are increasingly vulnerable to sophisticated cyber threats, the need for advanced security measures has never been greater. This paper analyzes the application of deep learning models in enhancing predictive threat detection within cybersecurity systems. By leveraging visual data, these models can monitor and identify abnormal behavior, enabling automated real-time threat responses. The study provides an overview of the fundamental concepts of deep learning and its relevance to cybersecurity, focusing on various architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Through an examination of current research and case studies, this paper highlights the effectiveness of deep learning in improving threat detection rates and response times, ultimately contributing to more robust cybersecurity frameworks. Additionally, the paper discusses the challenges and limitations of implementing deep learning in cybersecurity, offering insights into future research directions.
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