基于IRT的网络安全意识自适应测试技术
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摘要
为有效度量个人网络安全意识状态,根据项目反应理论和多阶段测验理论提出了网络安全意识自适应测试技术.首先,设计了基于"行业、岗位、人员"层级的知识库模型,根据模型给出了测试项目参数的计算方法.然后,引入反应时间的概念改进了逻辑斯蒂三参数模型.最后,提出了基于滑动窗口的组卷策略并给出了状态评估模型.结果表明:基于模型改进后的自适应测试的效能较传统模型测试和原逻辑斯蒂三参数模型的测试效能分别提高了34. 9%和8. 7%,为网络安全意识测试领域和其他领域计算机自适应测试技术的研究提供了参考.
To effectively measure the status of personal network security awareness,this paper proposed an adaptive testing of the network security awareness based on the items response theory (IRT) and multi-stage testing theory. First,a knowledge library model based on"industry,job,and personnel"was designed. According to the model,the calculation method of the item parameters was given. Second,the concept of reaction time was imported to improve the logistic-3-parameter model. Finally,the grouping strategy based on the sliding window and the state evaluation model was given. Results show that the improved adaptive testing performance is 34. 9% and 8. 7% higher than the traditional model testing and the original logistic-3-parameter model testing,respectively. It provides a reference for the field of testing on network security awareness and computerized adaptive testing technology.
To effectively measure the status of personal network security awareness,this paper proposed an adaptive testing of the network security awareness based on the items response theory (IRT) and multi-stage testing theory. First,a knowledge library model based on"industry,job,and personnel"was designed. According to the model,the calculation method of the item parameters was given. Second,the concept of reaction time was imported to improve the logistic-3-parameter model. Finally,the grouping strategy based on the sliding window and the state evaluation model was given. Results show that the improved adaptive testing performance is 34. 9% and 8. 7% higher than the traditional model testing and the original logistic-3-parameter model testing,respectively. It provides a reference for the field of testing on network security awareness and computerized adaptive testing technology.
引文
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[8]PARTHIBAN S,RODRIGUES P.Kuder-Richardson reputation coefficient based reputation mechanism for isolating root node attack in MANETs[J].Indian Journal of Science&Technology,2015,8(15):1-9.
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[10]路鹏.计算机自适应测试若干关键技术研究[D].长春:东北师范大学,2012.LU P.The research on technologies of computer adaptive testing[D].Changchun:Northeast Normal University,2012.
[11]UNLUA,SCHREPP M.Untangling comparison bias in inductive item tree analysis based on representative random quasi-orders[J].Mathematical Social Sciences,2015,76:31-43.
[2]BOWEN B M,DEVARAJAN R,STOLFO S.Measuring the human factor of cyber security[C]∥IEEE International Conference on Technologies for Homeland Security.New York:IEEE,2011:230-235.
[3]LORD F M.Applications of item response theory to practical testing problems[J].Erlbaum,1980,1:274.
[4]HOL A M,VORST H C M,MELLENBERGH G J.Computerized adaptive testing of personality traits[J].Zeitschrift Für Psychologie,2008,216(1):12-21.
[5]PRIMI C,MORSANYI K,CHIESI F,et al.The development and testing of a new version of the cognitive reflection test applying item response theory(IRT)[J].Journal of Behavioral Decision Making,2016,29(5):453-469.
[6]齐斌,邹红霞,王宇,等.浅析国内外网络安全意识教育知识体系[J].网络安全技术与应用,2017(6):18-20.QI B,ZOU H X,WANG Y,et al.The analyses of the domestic and foreign network security awareness education knowledge system[J].Journal of Network Security Technology and Applications,2017(6):18-20.(in Chinese)
[7]KIM S,FELDT L S.The estimation of the IRT reliability coefficient and its lower and upper bounds,with comparisons to CTT reliability statistics[J].Asia Pacific Education Review,2010,11(2):179-188.
[8]PARTHIBAN S,RODRIGUES P.Kuder-Richardson reputation coefficient based reputation mechanism for isolating root node attack in MANETs[J].Indian Journal of Science&Technology,2015,8(15):1-9.
[9]XU P,LIN W,LIU F,et al.Competitive regulation of IPO4 transcription by ELK1 and GABP[J].Gene,2017,613:30-38.
[10]路鹏.计算机自适应测试若干关键技术研究[D].长春:东北师范大学,2012.LU P.The research on technologies of computer adaptive testing[D].Changchun:Northeast Normal University,2012.
[11]UNLUA,SCHREPP M.Untangling comparison bias in inductive item tree analysis based on representative random quasi-orders[J].Mathematical Social Sciences,2015,76:31-43.