Malicious applications can be introduced to attack users and services so as to gain financial rewards, individuals＇ sensitive information, company and government intellectual property, and to gain remote control of s...Malicious applications can be introduced to attack users and services so as to gain financial rewards, individuals＇ sensitive information, company and government intellectual property, and to gain remote control of systems. However, traditional methods of malicious code detection, such as signature detection, behavior detection, virtual machine detection, and heuristic detection, have various weaknesses which make them unreliable. This paper presents the existing technologies of malicious code detection and a malicious code detection model is proposed based on behavior association. The behavior points of malicious code are first extracted through API monitoring technology and integrated into the behavior; then a relation between behaviors is established according to data dependence. Next, a behavior association model is built up and a discrimination method is put forth using pushdown automation. Finally, the exact malicious code is taken as a sample to carry out an experiment on the behavior＇s capture, association, and discrimination, thus proving that the theoretical model is viable.展开更多
The purpose of our study was to assess drinking status in middle school students and to understand the associated factors. The adjusted drinking rates were 50.9%, 39.8%, and 15.1% for lifetime, past-year, and current ...The purpose of our study was to assess drinking status in middle school students and to understand the associated factors. The adjusted drinking rates were 50.9%, 39.8%, and 15.1% for lifetime, past-year, and current drinking, respectively.展开更多
Discovering the hierarchical structures of differ- ent classes of object behaviors can satisfy the requirements of various degrees of abstraction in association analysis, be- havior modeling, data preprocessing, patte...Discovering the hierarchical structures of differ- ent classes of object behaviors can satisfy the requirements of various degrees of abstraction in association analysis, be- havior modeling, data preprocessing, pattern recognition and decision making, etc. In this paper, we call this process as associative categorization, which is different from classical clustering, associative classification and associative cluster- ing. Focusing on representing the associations of behaviors and the corresponding uncertainties, we propose the method for constructing a Markov network （MN） from the results of frequent pattern mining, called item-associative Markov net- work （IAMN）, where nodes and edges represent the frequent patterns and their associations respectively. We further dis- cuss the properties of a probabilistic graphical model to guar- antee the IAMN＇s correctness theoretically. Then, we adopt the concept of chordal to reflect the closeness of nodes in the IAMN. Adopting the algorithm for constructing join trees from an MN, we give the algorithm for IAMN-based associa- tive categorization by hierarchical bottom-up aggregations of nodes. Experimental results show the effectiveness, efficiency and correctness of our methods.展开更多
基金supported by the National Natural Science Foundation of China (Nos. 61272033 and 61272405)
文摘Malicious applications can be introduced to attack users and services so as to gain financial rewards, individuals＇ sensitive information, company and government intellectual property, and to gain remote control of systems. However, traditional methods of malicious code detection, such as signature detection, behavior detection, virtual machine detection, and heuristic detection, have various weaknesses which make them unreliable. This paper presents the existing technologies of malicious code detection and a malicious code detection model is proposed based on behavior association. The behavior points of malicious code are first extracted through API monitoring technology and integrated into the behavior; then a relation between behaviors is established according to data dependence. Next, a behavior association model is built up and a discrimination method is put forth using pushdown automation. Finally, the exact malicious code is taken as a sample to carry out an experiment on the behavior＇s capture, association, and discrimination, thus proving that the theoretical model is viable.
基金the financial support from Pernod Ricard (China)
文摘The purpose of our study was to assess drinking status in middle school students and to understand the associated factors. The adjusted drinking rates were 50.9%, 39.8%, and 15.1% for lifetime, past-year, and current drinking, respectively.
文摘Discovering the hierarchical structures of differ- ent classes of object behaviors can satisfy the requirements of various degrees of abstraction in association analysis, be- havior modeling, data preprocessing, pattern recognition and decision making, etc. In this paper, we call this process as associative categorization, which is different from classical clustering, associative classification and associative cluster- ing. Focusing on representing the associations of behaviors and the corresponding uncertainties, we propose the method for constructing a Markov network （MN） from the results of frequent pattern mining, called item-associative Markov net- work （IAMN）, where nodes and edges represent the frequent patterns and their associations respectively. We further dis- cuss the properties of a probabilistic graphical model to guar- antee the IAMN＇s correctness theoretically. Then, we adopt the concept of chordal to reflect the closeness of nodes in the IAMN. Adopting the algorithm for constructing join trees from an MN, we give the algorithm for IAMN-based associa- tive categorization by hierarchical bottom-up aggregations of nodes. Experimental results show the effectiveness, efficiency and correctness of our methods.
