Anomaly Detection Approaches for Secure Cloud Reference Architectures in Legal Metrology
Anomaly Detection Approaches for Secure Cloud Reference Architectures in Legal Metrology
复制标题
法定计量中安全云参考架构的异常检测方法
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Jean
中科院分区：
文献类型：
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作者：
A. Oppermann;F. G. Toro;F. Thiel;Jean
保护计算机系统免受各种威胁是一个不可能完成的挑战。尽管在法律的计量领域，应确保可信赖的计算机系统进行测量。在分布式环境中，测量仪器不能简单地断开以保证其安全性。然而，能够不断地监视计算机系统，以推断正常的系统行为，可以是一个特别有前途的方法来保护这样的系统。在检测到异常的情况下，系统会对其进行评估，以衡量检测到的事件的严重性，并将其分为三个不同的类别：绿色、黄色和红色。所提出的异常检测模块可以检测针对云计算环境中的分布式应用程序的攻击，使用模式识别聚类以及统计方法。经验不足和经验丰富的攻击都已经过测试，结果是
Securing Computer Systems against all kind of threats is an impossible challenge to fulfill. Although, in the field of Legal Metrology, it shall be assured that one can rely on the measurement carried out by a trusted computer system. In a distributed environment, a measurement instrument cannot be simply disconnected to gurantee its security. However, being able to monitor the computer systems constantly in order to deduce a normal system behaviour, can be a particular promising approach to secure such systems. In cases of detected anomalies, the system evaluates them to measure the severity of the detected incident and place it into three different categories: green, yellow and red. The presented Anomaly Detection Module can detect attacks against distributed applications in an cloud computing environment, using pattern recognition for clustering as well as statistical approaches. Both, inexperienced and experienced attacks have been tested and results are