Paper Contents
Abstract
This paper explores the potential of deep learning algorithms in analyzing vast amounts of cybersecurity data to detect and mitigate threats. The proposed approach employs convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to classify and predict malicious activity with high accuracy. This paper presents a novel architecture that combines CNN and LSTM layers, offering improved feature extraction and temporal analysis.
Copyright
Copyright © 2024 Jyoti Rana. This is an open access article distributed under the Creative Commons Attribution License.
Paper ID:
IJPREMS41100029426
ISSN:
2321-9653
Publisher:
ijprems