Aiming at the problem that the attack chain model for the attack phase is too small to indicate the means of attack, an Advanced Persistent Threat (APT) Hierarchical Attack Representation Model (APT-HARM) was proposed. By summarizing the analysis of a large number of published APT event reports and reference APT attack chain model and HARM, the APT attack was divided into into two layers, the upper layer attack chain and the lower layer attack tree, which were formally defined. Firstly, the APT attack was divided into four stages:reconnaissance, infiltration, operation and exfiltration and the characteristics of each stage were studied. Then, the attack methods in each stage were studied, and the attack tree was composed according to its logical relationship. APT attacks were carried out in stages according to the attack chain, and the attack of each stage was performed in accordance with the attack tree. The case study shows that the model has the advantages of reasonable granularity classification and better attack description compared to the attack chain model. APT-HARM formally defines the APT attack, which provides an idea for the prediction and prevention of APT attacks.

This paper analyzed the metadata processing in PVFS2, and took the remove operation as an example. To find out the bottlenecks in the remove operation, the time of each step was tested. And an optimization method to reduce the communication number by placing judgmental process on the server side was proposed, which is also suitable for other metadata operations. The optimization method was implemented in PVFS2. Compared with the original remove operation, this proposed method shows about 10% improvement in performance.

Feature selection is one of the important issues in the machine learning and pattern recognition. For highdimensional data, the new feature selection method based on Fisher criterion and feature clustering was proposed. Firstly, the features that have more discrimination information based on Fisher criterion were selected. Then hierarchical clustering in the preselected subset was adopted. Finally the irrelevant and redundant features were removed. The experimental results show that the proposed algorithm is an effective method for feature selection.