软件异类入侵数据自我识别强度云检测方法  

Cloud Detection Method for Self-Recognition Intensity of Software Heterogeneous Intrusion Data

作　　者：黄振华 李春华[2] 张波 刘翠焕[1] HUANG Zhen-hua;LI Chun-hua;ZHANG Bo;LIU Cui-huan(Software Institute of Hebei Engineering and Technology Institute,Hebei Shijiazhuang 050091,China;School of Information Science and Engineering,Hebei University of Science and Technology,Shijiazhuang Hebei 050018,China)

机构地区：[1]河北工程技术学院软件学院,河北石家庄050091 [2]河北科技大学信息科学与工程学院,河北石家庄050018

出　　处：《计算机仿真》2021年第9期366-370,共5页Computer Simulation

摘　　要：由于软件内部组网中数据类型复杂,增加了异类入侵数据的检测难度。目前现有的入侵数据检测方法存在误报、漏报率不理想的问题。为提高入侵数据检测的精准度,提出软件异类入侵数据自我识别强度云检测方法。通过数据收集、入侵分析和响应处理完成软件异类入侵数据识别;根据入侵对象与方式划分数据类型,获取数据编码,确定数据强度;考虑软件组网的周期性,优化源程序数据传输路径,提高检测入侵数据的精准度;利用云检测,识别入侵数据强度信息,根据处理结果和识别结果实现检测。仿真验结果表明,所提方法能够有效提高数据的检测率,降低误差率及漏报率。Due to the complex data types in software internal networking, it is difficult to detect heterogeneous intrusion data.Currently, the detection method of intrusion data has some defects, such as high false alarm rate and missed rate.This paper proposes a cloud detection method for the self-identification intensity of software heterogeneous intrusion data for improving the accuracy of intrusion data detection.According to the results of data collection, intrusion analysis and response processing, the identification of software heterogeneous intrusion data was completed.The data types were divided by intrusion objects and methods, data coding was obtained, and data intensity was determined.Based on the periodicity of software networking, the transmission path of source program data was optimized to improve the accuracy of intrusion detection data.Cloud detection was adopted to identify the intensity information of intrusion data.Eventually, the detection was achieved according to the results of processing and identification.The simulation results show that the method has high data detection rate, low error rate and missed rate.

关 键 词：自我识别 识别强度 异类入侵 数据检测 云检测 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]