基于麻雀搜索算法优化的神经网络入侵检测研究

Yue Li, Yunfa Huang, Peiting Xu, Zengjin Liu
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引用次数: 0

摘要

近年来,受新冠肺炎疫情影响,大多数人在家办公、在线学习,互联网流量激增。与此同时,网络攻击也越来越频繁。入侵检测系统作为系统的第二道防火墙,通过网络数据监控和各种报警机制,帮助用户及时发现安全威胁并采取相应的措施。为了改进入侵检测系统,提出了一种利用麻雀搜索算法对反向传播神经网络进行优化的方法。该模型采用Min-Max缩放和Borderline SMOTE过采样算法对数据进行预处理,并采用帐篷图初始化麻雀种群搜索算法。最后,与其他传统机器学习模型相比,我们选择召回率作为核心指标,精度作为次要指标,f1_score作为辅助指标。实验结果表明,该模型具有较好的召回率和f1_score,表明该模型在入侵检测中具有较好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research of Intrusion Detection Based on Neural Network Optimized by Sparrow Search Algorithm
In recent years, due to the impact of the COVID-19, most people work from home and study online, resulting in a surge in internet traffic. At the same time, cyber attacks are occurring more frequently. As the second firewall of the system, intrusion detection system can help users discover security threats in time and take corresponding measures through network data monitoring and various alarm mechanisms. To improve the intrusion detection system, a proposal has been made to optimize back propagation neural network using the sparrow search algorithm. This model uses Min-Max scaling and Borderline SMOTE oversampling algorithm to preprocess data, and uses tent map to initialize the population of sparrow search algorithm. Finally, compared with other traditional machine learning models, we choose recall as the core indicator, precision as the secondary indicator, and f1_score as the auxiliary indicator. Experimental results indicate that our model exhibits an improved recall and f1_score, indicating that our model exhibits superior performance in intrusion detection.
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