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基于蝙蝠优化算法的网络入侵检测模型
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(完整内容请下载后查看)Computer Science and Application 计
Network Intrusion Detection Model Based
on Bat Optimization Algorithm
Qingjie Zhao1, Longge Wang1*, Jie Li1, Junyang Yu1,2
1College of Software, Henan University, Kaifeng Henan
2State Key Laboratory of Network and Exchange Technology, Beijing University of Posts and Telecommunications,
Beijing
Received: Oct. 15th, 2018; accepted: Oct. 26th, 2018; published: Nov. 2nd, 2018
Abstract
Network intrusion has the characteristics of sudden and concealment, and the traditional tech-
nology is difficult to describe the law of change, which leads to a very low accuracy of intrusion
detection. In order to improve the accuracy of intrusion detection and reduce the false detection
rate, a bat optimization algorithm based on dynamic adaptive weight and Cauchy mutation was
proposed to optimize the neural network intrusion detection model. It is necessary to collect the
data of the intrusion network and then import the data into the neural network to learn. The bat
optimization algorithm is used to optimize the parameters of the network model. Finally, the KDD
CUP 99 dataset is selected to simulate the network intrusion detection. The results show that the
proposed model can obtain ideal network intrusion detection rate and false detection rate.
Keywords
Bat Optimization Algorithm, Neural Network, Intrusion Detection Model, Model Parameters
基于蝙蝠优化算法的网络入侵检测模型
赵青杰1,王龙葛1*,李 捷1,于俊洋1,2
1河南大学软件学院,河南 开封
2北京邮电大学网络与交换技术国家重点实验室,北京
收稿日期:2018年10月15日;录用日期:2018年10月26日;发布日期:2018年11月2日
摘 要
网络入侵具有突发性和隐蔽性等特点,传统的技术很难描述其变化规律,这导致入侵检测正确率非常的
*通讯作者。
文章引用: 赵青杰, 优化算法的网络入侵检测模型[J]. 计算机科学与应用, 2018,
8(11): 1650-1656. DOI
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