启发式遗传算法下密集空间网络传输异常检测  

Transmission Anomaly Detection in Dense Spatial Networks Based on Heuristic Genetic Algorithm

作　　者：邓伦丹[1] 华鑫[2] DENG Lun-dan;HUA Xin(College of Science and Technology,Nanchang University,Gongqingcheng Jiangxi 332020,China;Nanchang University,Nanchang Jiangxi 330047,China)

机构地区：[1]南昌大学科学技术学院,江西共青城332020 [2]南昌大学,江西南昌330047

出　　处：《计算机仿真》2023年第12期441-445,共5页Computer Simulation

摘　　要：随着互联网技术的广泛应用,网络信息传输技术也飞速发展、需求逐年递增,网络传输异常检测的需求也日益增加。针对当前网络传输异常检测算法运算速度慢、检测率低、误差大等问题,提出了基于启发式算法的密集空间网络传输异常检测算法。首先采用反向传播算法对基本检测原理进行优化;其次基于时间衰减函数提取异常数据的特征;然后基于启发式遗传算法设计网络传输异常检测算法模型;最后对遗传算法的适应性参数进行性能优化。上述算法使用网络数据库进行实验,实验结果表明,相比其它异常检测方法,所提算法将误检率降低近10%、漏检率降低至少7%,极大的减少了网络传输异常带来的影响,促进了启发式遗传算法的研究,推动了网络传输异常检测技术的发展和应用。With the wide application of Internet technology,network information transmission technology is also developing rapidly,and the demand for network transmission anomaly detection is increasing year by year.Aiming at the problems of low operation speed,low detection rate and large error of current network transmission anomaly detec⁃tion algorithms,this paper proposes a dense space network transmission anomaly detection algorithm based on heuris⁃tic algorithms.Firstly,the backpropagation algorithm was used to optimize the basic detection principle.Secondly,the feature of abnormal data was extracted based on the time decay function.Thirdly,the network transmission anom⁃aly detection algorithm model was designed based on a heuristic genetic algorithm.Finally,the adaptive parameters of the genetic algorithm were optimized.The algorithm uses the network database for experiments.The experimental re⁃sults show that compared with other anomaly detection methods,the proposed algorithm reduces the false detection rate by nearly 10%and the missed detection rate by at least 7%,greatly reducing the impact of network transmission anomalies,promoting the research of heuristic genetic algorithm,and promoting the development and application of network transmission anomaly detection technology.

关 键 词：遗传算法 启发式算法 网络传输 异常检测 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]