Network Security Situation Prediction Based on CCQPSO-BiLSTM

被引：0
|
作者：
Sun, Junfeng [1 ]
Li, Chenghai [1 ]
Song, Yafei [1 ]
机构：
[1] Air Force Engn Univ, Coll Air & Missile Def, Xian, Peoples R China
来源：
2022 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, COMPUTER VISION AND MACHINE LEARNING (ICICML) | 2022年
基金：
中国国家自然科学基金;
关键词：
network security situation prediction; quantum particle swarm algorithm; chaotic map; crossover operator; bidirectional long short term memory network;
D O I：
10.1109/ICICML57342.2022.10009708
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In order to improve the prediction accuracy and speed of the model, a network security situation prediction model based on chaotic cross quantum particle swarm optimization optimization bidirectional long short term memory (CCQPSO-BiLSTM) network is proposed. Logistics is used to map the initial population as a chaotic sequence to search for the optimal solution, and the information exchange of individuals in the population is carried out through the vertical crossover operation. Finally, the hyperparameters of the model are optimized by CCQPSO. Through four standard test functions, the effectiveness of introducing chaotic mapping and crossover operations into quantum particle swarm optimization is verified. The experimental results show that the fitting degree of the proposed prediction method can reach 0.9977, and the convergence speed is greatly improved compared with the comparison algorithm.
引用
收藏
页码：445 / 451
页数：7
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