计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
2期
103-107,124
,共6页
通用漏洞评估系统(CVSS)%指标量化%反向传播(BP)神经网络%评估模型%MATLAB
通用漏洞評估繫統(CVSS)%指標量化%反嚮傳播(BP)神經網絡%評估模型%MATLAB
통용루동평고계통(CVSS)%지표양화%반향전파(BP)신경망락%평고모형%MATLAB
Common Vulnerability Scoring System(CVSS)%indicator quantified%Back Propagation(BP)neural network%evaluation model%MATLAB
针对CVSS v2.0主观性强、操作性差,建立自动化评估模型困难的问题,提出在CVSS v2.0评估体系的基础上,改进其评价指标体系,把评价指标分为主客观两类;使用BP神经网络自学习原理再次优化评价因子;并建立基于BP神经网络的自动化评估模型,快速地对输入指标的特征做逼近实效的量化。通过MATLAB仿真验证了该方法的有效性、准确性与可行性。
針對CVSS v2.0主觀性彊、操作性差,建立自動化評估模型睏難的問題,提齣在CVSS v2.0評估體繫的基礎上,改進其評價指標體繫,把評價指標分為主客觀兩類;使用BP神經網絡自學習原理再次優化評價因子;併建立基于BP神經網絡的自動化評估模型,快速地對輸入指標的特徵做逼近實效的量化。通過MATLAB倣真驗證瞭該方法的有效性、準確性與可行性。
침대CVSS v2.0주관성강、조작성차,건립자동화평고모형곤난적문제,제출재CVSS v2.0평고체계적기출상,개진기평개지표체계,파평개지표분위주객관량류;사용BP신경망락자학습원리재차우화평개인자;병건립기우BP신경망락적자동화평고모형,쾌속지대수입지표적특정주핍근실효적양화。통과MATLAB방진험증료해방법적유효성、준학성여가행성。
Considering that there are several drawbacks included in CVSS 2.0, such as strongly subjectivity, inefficient maneuverability, the difficulty to create automated assessment model, the evaluation index system is improved based on CVSS 2.0 evaluation system. And the evaluation index system is divided into two parts which are objective category and subjective category. It optimizes evaluation factor with principles of BP neural network self-learning and builds an automa-tion evaluation model based on BP neural network, then quantizes the input indicators characteristic into approximation of effectiveness rapidly. Finally the effectiveness, accuracy and feasibility of the method are proved by MATLAB simulation.