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The i.i.d assumption is the corner stone of most conventional machine learning algorithms. However, reducing the bias and variance of the learning model on the i.i.d dataset may not help the model to prevent from their failure on the adversarial samples, which are intentionally generated by either the malicious users or its rival programs. This paper gives a brief introduction of machine learning and adversarial learning, discussing the research frontier of the adversarial issues noticed by both the machine learning and network security field. We argue that one key reason of the adversarial issue is that the learning algorithms may not exploit the input feature set enough, so that the attackers can focus on a small set of features to trick the model. To address this issue, we consider two important classes of classifiers. For random forest, we propose a type of random forest called Weighted Random Forest (WRF) to encourage the model to give even credits to the input features. This approach can be further improved by careful selection of a subset of trees based on the clustering analysis during the run time. For neural networks, we propose to introduce extra soft constraints based on the weight variance to the objective function, such that the model would base the classification decision on more evenly distributed feature impact. Empirical experiments show that these approaches can effectively improve the robustness of the learnt model against their baseline systems.

In order to strengthen the scientific management of saline-alkali land and the accurate management of agricultural production, a scientific data governance platform for saline-alkali land was developed. Based on the data accumulation of big data platform, the relationship between wheat growth and meteorology was taken as the research object. Thirteen variables including atmospheric pressure, temperature, light, and precipitation were extracted from surface meteorological data for correlation analysis, and temperature, precipitation, and sunshine were selected as the characteristic variables; through discretization processing, we have finally determined the three indicators that can be categorized: accumulative temperature, sunshine hours, and temperature. Finally, the three indicators are combined with months to build a model of farming season and weather based on Apriori. The results show that when judging the farming season with the month as the index, the accuracy of the model is between 78.81 and 100%. When the temperature or accumulated temperature is taken as the index to judge the winter wheat farming time, the accuracy of the model is above 90%. This shows that accurate analysis of farming season can be achieved through big data correlation analysis, which provides technical support for the timely adoption of agricultural production.