FusedCNN-LSTM-AttNet: A Neural Network Model for Cyber Security Situation Prediction
Authors: 
Zihan Xiong, Yuxuan Li, Jun Chen, Dabei ChenAuthors Info & Claims
Zihan Xiong, Yuxuan Li, Jun Chen, Dabei ChenAuthors Info & ClaimsPages 207 - 210
Abstract
This paper presents a novel network security situational prediction model, FusedCNN-LSTM-AttNet, which integrates Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Attention mechanisms. The model leverages continuous time sequences to forecast future network security conditions. Through validation on the KDD99 dataset, FusedCNN-LSTM-AttNet exhibits significant advantages in prediction accuracy and effectiveness, particularly excelling in capturing predictive trends. Experimental results demonstrate the high feasibility and practicality of the FusedCNN-LSTM-AttNet model for predicting network security conditions.
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- FusedCNN-LSTM-AttNet: A Neural Network Model for Cyber Security Situation Prediction
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Published In
October 2023
446 pages
ISBN:9798400716683
DOI:10.1145/3640912
Copyright © 2023 ACM.
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Published: 22 February 2024
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CNML 2023
CNML 2023: 2023 International Conference on Communication Network and Machine Learning
October 27 - 28, 2023
Zhengzhou, China
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