Quantifying security risk is an important and yet difficult task in enterprise network security management. While metrics exist for individual software vulnerabilities, there is currently no standard way of aggregating such metrics. We present a model that can be used to aggregate vulnerability metrics in an enterprise network, producing quantitative metrics that measure the likelihood breaches can occur within a given network configuration. A clear semantic model for this aggregation is an important first step toward a comprehensive network security metric model. We utilize existing work in attack graphs and apply probabilistic reasoning to produce an aggregation that has clear semantics and sound computation. We ensure that shared dependencies between attack paths have a proportional effect on the final calculation. We correctly reason over cycles, ensuring that privileges are evaluated without any self-referencing effect. We introduce additional modeling artifacts in our probabilistic graphical model to capture and account for hidden correlations among exploit steps. The paper shows that a clear semantic model for aggregation is critical in interpreting the results, calibrating the metric model, and explaining insights gained from empirical evaluation. Our approach has been rigorously evaluated using a number of network models, as well as data from production systems.

In recent years, the number of malware on the Android platform has been increasing, and with the widespread use of code obfuscation technology, the accuracy of antivirus software and traditional detection algorithms is low. Current state-of-the-art research shows that researchers started applying deep learning methods for malware detection. We proposed an Android malware detection algorithm based on a hybrid deep learning model which combines deep belief network (DBN) and gate recurrent unit (GRU). First of all, analyze the Android malware; in addition to extracting static features, dynamic behavioral features with strong antiobfuscation ability are also extracted. Then, build a hybrid deep learning model for Android malware detection. Because the static features are relatively independent, the DBN is used to process the static features. Because the dynamic features have temporal correlation, the GRU is used to process the dynamic feature sequence. Finally, the training results of DBN and GRU are input into the BP neural network, and the final classification results are output. Experimental results show that, compared with the traditional machine learning algorithms, the Android malware detection model based on hybrid deep learning algorithms has a higher detection accuracy, and it also has a better detection effect on obfuscated malware.

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Wheat spike number, which could be rapidly and accurately estimated by the image processing technology, serves as the basis for crop growth monitoring and yield prediction. In this research, simple linear iterative clustering (SLIC) was performed for superpixel segmentation of the digital images of field-grown wheat. Firstly, certain characteristic color parameters were extracted and analyzed from the digital images, and the classifiers with the highest accuracy were chosen for subsequent image classification. Next, the main body of wheat spike was extracted through a series of morphological transformation and estimate was performed for each region. Backbone of the head was extracted, and the number of inflection points of backbone was detected. Then the wheat spike number was determined by combining the estimate of inflection points of backbone and the estimate for each region. Finally, the wheat spike number estimate was verified under four nitrogen fertilizer levels. The results were as follows: (1) Super green value (Eg) and normalized red green index (Dgr) were used as classification features to recognize wheat spikes, soil and leaves; (2) compared with pixel-based image processing, wheat spike recognition effect was much better after superpixel segmentation, as the main body of wheat spike extracted was more clear and morphology more intact; and (3) wheat plants had better growth under high nitrogen fertilizer level, and the accuracy of wheat spike number estimation was also the highest, which was 94.01%. The growth status was the worst under no nitrogen fertilizer application, and the accuracy of wheat spikes number estimation was also the lowest, which was only 80.8%. After excluding the no nitrogen condition, the accuracy of wheat spikes number estimation among mixed samples with more uniform growth status was up to 93.8%, which was an increase by 10.1% than before the exclusion. Wheat spikes number estimate based on superpixel segmentation and color features was a rapid and accurate method that was applicable to the field environment. However, this method was not recommended for use when the growth status of wheat was poor or of high heterogeneity. The findings provided reference for field-grown wheat yield estimate.

Since the 1990s, teachers have become more prone to job burnout as education reforms worldwide have intensified their workload. We examined the relationships between teacher burnout, transformational leadership, and psychological empowerment among 339 primary and secondary school teachers in China. Results show that transformational leadership and psychological empowerment negatively predicted teacher burnout, and the transformational leadership–teacher burnout relationship was mediated by psychological empowerment. Of the four dimensions of psychological empowerment, the meaning, selfdetermination, and competence dimensions significantly mediated the transformational leadership–teacher burnout relationship. Thus, transformational leadership alleviated teacher burnout because it enhanced teachers' sense of work meaningfulness, self-determination, and competence.

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To solve the problems of updating sub-secrets or secrets as well as adding or deleting agents in the quantum secret sharing protocol, we propose a two-particle transform of Bell states, and consequently present a novel dynamic quantum secret sharing protocol. The new protocol can not only resist some typical attacks, but also be more efficient than the existing protocols. Furthermore, we take advantage of the protocol to establish the dynamic secret sharing of a quantum state protocol for two-particle maximum entangled states.