Administrators usually take some network security metrics as important bases to measure network security. Common Vulnerability Scoring System (CVSS) is one of the generally accepted network measurement method. Aiming at the problem that the existing network security measurement based on CVSS could not accurately measure the probability and the impact of network attack at the same time, an improved base metric algorithm based on dependency relationship graph and CVSS was proposed. Firstly, the dependency relationship of the vulnerability nodes in an attack graph was explored to build the dependency relationship graph. Then, the base metric algorithm of the vulnerability in CVSS was modified according to the dependency relationship. Finally, the vulnerability scores in the whole attack graph were aggregated to obtain the probability and the impact of network attack. The results of simulation with simulated attacker show that the proposed algorithm is superior to the algorithm of aggregating CVSS scores in terms of accuracy and credibility, and can get measurement results closer to the actual simulation results.

In complex environment, existing pedestrian detection methods can not be very good to achieve high recall rate and efficient detection. To solve this problem, a pedestrian detection method based on Convolutional Neural Network (CNN) was proposed. Firstly, pedestrian locations in input images were initially detected with single step detection upgrade network (YOLOv2) derived from CNN. Secondly, a network with target classification and bounding box regression was designed to cascade with YOLOv2 network, which made reclassification and regression of pedestrian location initially detected by YOLOv2, to reduce error detections and increase recall rate. Finally, a Non-Maximum Suppression (NMS) method was used to remove redundant bounding boxes. The experimental results show that, in INRIA and Caltech dataset, the proposed method increases recall rate by 3.3 percentage points, and the accuracy is increased by 5.1 percentage points compared with original YOLOv2. It also reached a speed of 11.6FPS (Frames Per Second) to realize real-time detection. Compared with the existing six popular pedestrian detection methods, the proposed method has better overall performance.