Name
Abstract | ||
|---|---|---|
Malware in form of Internet worms, computer viruses, and Trojan horses poses a major threat to the security of networked systems. So how to describe the behavior knowledge of malware is an interesting and meaningful work. In recent years, different ontology technologies have been proposed to represent domain knowledge. In the study, we apply ontology techniques into the field of malware detection, and propose the malware detection method based on ontology. This method is based on the behavior of malicious code, and makes a knowledge representation of the malware behaviors from a variety of perspectives. We use the common behaviors of individuals to represent the behaviors of a malware family, and use the ontology reasoning mechanism to detect unknown malware samples. Experiments show that the method has high malicious code detection rate and low false alarm rate. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICMLC.2017.8107737 | 2017 International Conference on Machine Learning and Cybernetics (ICMLC) |
Keywords | Field | DocType |
Ontology,Malware,Dynamic behavior,Rule | Ontology (information science),Ontology,Knowledge representation and reasoning,Domain knowledge,Computer science,Computer virus,Information security,Artificial intelligence,Malware,Machine learning,The Internet | Conference |
Volume | ISSN | ISBN |
1 | 2160-133X | 978-1-5386-0409-0 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xia Xiao-Ling | 1 | 0 | 0.34 |
| Yuxin Ding | 2 | 237 | 21.52 |
| Jiang Jing-Zhi | 3 | 0 | 0.34 |
| Rong Zeng | 4 | 25 | 10.40 |