The Coding Technology of Operation and Management Risk Characteristics Based on the InDepth Learning In the enterprise risk identification project, the data has the characteristics of the complex data sources and the diverse risks. The traditional feature extraction methods cannot cope with the multiple complex data sources at the same time. In this study, the risk feature extraction based on the single relationship of theindepth learning and the feature coding technology of the risk atlas considering the time series can effectively extract the feature vectors of the risk atlas and avoid them at the same time. It avoids the complexity and blindness of the artificial feature extraction. And it is verified by a largescale organizationxe2x80x99s experiment on the enterprisexe2x80x99s overdue risk. Under the same model framework, the characteristics generated by using the risk feature coding technology are better than those given by the traditional experts under the modelxe2x80x99s various indicators, which provide the strong support for the followup risk identification.

Cyber Profiles Based Risk Prediction of Application Systems for Effective Access Control Application systems maintain critical sensitive information of an enterprise and especially huge number of data with specific ownership. Unauthorized modification or deletion of data caused by cyber attacks may bring tremendous loses for enterprises. To reduce the damage of cyber attacks, existing techniques have been proposed to predict the potential risk of external attacks at the level of an enterprise, a user, or a machine. However, risk prediction has not been conducted at the level of application systems, which may suffer from external attacks or insider threats. This paper proposes a model based on machine learning to predict whether the application systems of an enterprise have the risk of unauthorized access by using a cyber profile. In particular, the cyber profile is composed of features extracted from the information of the three domains in cyberspace: Information Infrastructure domain, Data domain, and Application domain. The core idea of the model selects the most significant features that have a large impact on the occurrence of unauthorized access to application systems. At last, by using a limited number of selected features, high forecast accuracy is achieved. These results verify the effectiveness of the prediction model, which can potentially be exploited to guide the adjustment of access control policies for effective access control.