Aiming at the problems of low positioning precision and efficiency of fingerprint positioning methods based on Weighted K-Nearest Neighbor （WKNN） and machine learning algorithms， a fingerprint positioning method based on Measurement Report （MR） signal clustering was proposed. Firstly， MR signals were divided into three attributes： indoor， road and outdoor. Then， by using the Geographic Information System （GIS） information， the grids were divided into building， road and outdoor sub-regions， and MR data with different attributes were placed in the sub-regions with corresponding attributes. Finally， with the help of K-Means clustering algorithm， MR signals in the grid were clustered and analyzed to create virtual sub-regions under the sub-region， and WKNN algorithm was used to match MR test samples. Besides， the average positioning accuracy was calculated by using the Euclidean distance， and the positioning performance of the proposed method was tested by some MR data in the production environment. Experimental results show that the proportion of 50 m positioning error of the proposed method is 71.21%， which is 2.64 percentage points higher than that of WKNN algorithm， and the average positioning error of the proposed method is 44.73 m， which is 7.60 m lower than that of WKNN algorithm. It can be seen that the proposed method has good positioning precision and efficiency， and can meet the positioning requirements of MR data in the production environment.

Low-rate Denial of Service （LDoS） attack is an improved form of Denial of Service （DoS） attack， which is difficult to detect due to its low average attack rate and strong concealment. To solve the above difficulty， a LDoS attack detection method based on Weighted Mean-Shift K-Means algorithm （WMS-Kmeans） under the architecture of Software-Defined Network （SDN） was proposed. Firstly， by obtaining the flow table information of OpenFlow switch， the six-tuple characteristics of LDoS attack traffic in SDN environment were analyzed and extracted. Then， the percentage error of average absolute value was used as the weight of the Euclidean distance in the mean shift clustering， and the resulting cluster center was used as the initial center of K-Means to cluster the flow table， so as to realize the detection of LDoS attacks. The experimental results show that the proposed method has high detection performance against LDoS attacks in the SDN environment， with an average detection rate of 99.29%， an average false alarm rate of 1.97% and an average missing alarm rate of 0.69%.