摘要
传统恶意软件Web攻击检测方法信息采集能力较弱,检测准确率低。针对这一问题,基于AdaBoost算法研究了一种新的恶意软件Web攻击检测方法。识别恶意软件Web攻击信息,整合信息类别,实施大规模的数据操作,利用系统自主报警功能简化操作步骤,控制识别信息处于系统可操作范围内。以识别的信息为基础进行信息采集,并添加数据信息过滤功能,确保信息采集的准确性。在数据采集量到达一定程度时,及时采取数据筛选措施,避免干扰信号的影响,结合采集信息进行恶意软件Web攻击检测,剖析AdaBoost算法的操作器数学结构。实验结果表明,该检测方法能够在一定程度上提升恶意软件的分辨率,提高软件采集的准确率。
The traditional malware Web attack detection method has weak information collection ability and low detection accuracy.To solve this problem,a new detection method of malware Web attack is studied based on AdaBoost algorithm.Identify malware Web attack information,integrate information categories,implement large-scale data operations,simplify operation steps by using the system's independent alarm function,and control the identification information within the system's operational range.Information collection is based on the identified information,and data filtering function is added to ensure the accuracy of information collection.When the amount of data collected reaches a certain degree,data filtering measures should be taken in time to avoid the influence of interference signals.Combined with the collected information,malware Web attack detection should be carried out,and the mathematical structure of the operator of AdaBoost algorithm should be analyzed.Experimental results show that the detection method can improve the resolution of malware to a certain extent,and improve the accuracy of software acquisition.
作者
张彤
ZHANG Tong(The Engineering&Technical College of Chengdu University of Technology,Leshan 614000,China)
出处
《电子设计工程》
2020年第24期123-127,共5页
Electronic Design Engineering
