Name
Abstract | ||
|---|---|---|
Traditional malware detection usually relies on the detected file only, not considering the usage scenario. This paper introduces the patterns of user behaviors, in addition to the normal dynamic analysis of process behaviors. The maliciousness of unknown file is calculated by attack tree model and Bayesian algorithm based on the file behaviors and sources. We count the security weights of file sources where users download or copy files, indicating the use habits and the safety consciousness. The assessment value of host security is finally obtained by knowledge repository update and dynamic machine learning, helping users to detect the behavior pattern and reinforce the host security. Experiments show that the accuracy of malware detection increases with the improvement of user's safety habits. As a result, our model can detect malware and lead the user to use computer securely in a realistic way. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/GreenCom-iThings-CPSCom.2013.390 | GreenCom/iThings/CPScom |
Keywords | Field | DocType |
unknown file,dynamic knowledge repository-based security auxiliary system,invasive software,attack tree model,auxiliary system,unknown file maliciousness,copy file,trees (mathematics),file behavior,usage habits,file behaviors,bayes methods,learning (artificial intelligence),process behavior normal dynamic analysis,bayesian algorithm,safety consciousness,dynamic knowledge repository,malware detection,pattern of user behavior,traditional malware detection,user behavior,file source,dynamic machine learning,host security,malware detection increase,dynamic knowledge repository-based security,host security assessment value,file sources security weight,user behavior pattern,security weight,learning artificial intelligence | Behavioral pattern,Data mining,Bayesian algorithm,Computer science,Attack tree,Download,Malware | Conference |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Yang | 1 | 16 | 2.71 |
| Jinxia Wu | 2 | 8 | 2.23 |
| Shanyu Tang | 3 | 213 | 17.47 |
| Huanguo Zhang | 4 | 214 | 39.35 |