Risk Control System of Construction Engineering Based on Data Mining and Artificial Intelligence Technology Cloud computing provides a displaying and sharing platform for data mining technology. In order to prevent privacy disclosure, these data often contain artificially added risk control data, which makes the process of data sharing face the problem of deep mining of data. In terms of privacy protection, construction engineering risk control system is usually through the generalization way to complete the sharing of the accurate data, providing a condition for the followup accurate query process. At the same time, it also ensures that these data can be effectively predicted. According to the possible intersection or inclusion relationship between the generalized values, the generalized values are stratified and clustered in order to save the related important information of the construction project risk control. On this basis, this paper uses data mining technology (DMT) to design a construction project risk control system (CPRCS) based on data mining technology. Finally, through theoretical analysis and experimental comparison, this paper demonstrates the feasibility and effectiveness of the system.

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.