摘要
针对传统恶意软件攻击行为自主防护未能识别出恶意软件的数据特征,导致自主防护能力较差的问题,提出一种基于云计算的恶意软件攻击行为自主防护方法,通过Fourier变换方法利用滤波后网络数据得到振荡衰减规律,通过振荡衰减规律获取信息传输过程网络输出解析模型,获得高阶累积量切片函数;采用高阶累积量后置搜索方法对网络输出解析模型进行搜索,全面识别恶意软件攻击行为数据特征,通过FIR滤波方法处理云计算环境下网络数据,对网络数据干扰抑制;将恶意软件攻击行为数据特征通过向量序列形式表示,选取平方预测误差方法求解向量序列,将大于平方预测误差门限值的数据作为恶意软件攻击行为特征进行自主防护。仿真结果表明,恶意软件攻击行为自主防护性能较强。
In this article,an autonomous protection method for malware attack behavior was proposed.The Fourier transform method and the filtered network data were used to find the oscillation attenuation law.On this basis,the network output analytical model of information transmission process and the high-order cumulant slice function were obtained.The high-order cumulant post search method was used to search the network output analytical model,so as to comprehensively identify the data feature of malware attack behavior.Moreover,FIR data was used to process the network data in cloud computing environment,so that the network data interference was suppressed.The data features of malware attack behavior were represented in the form of vector sequence.In addition,the square prediction error method was used to solve the vector sequence.Finally,we adopted the data that were greater than threshold value of square prediction error as the malware attack behavior feature for autonomous protection.Simulation results show that the proposed method has strong self-protection performance.
作者
张果
ZHANG Guo(School of Medical Information and Engineering,Southwest Medical University,Luzhou Sichuan 646000,China)
出处
《计算机仿真》
北大核心
2020年第3期305-308,313,共5页
Computer Simulation
关键词
云计算
恶意软件
攻击行为
自主防护
滤波
数据特征
Cloud computing
Malware
Attack behavior
Autonomous protection
Filtering
Data feature
作者简介
张果(1981-),男(汉族),四川自贡人,硕士,助教,研究方向:软件工程。
