Randomizing smartphone malware profiles against statistical mining techniques
DBSec'12 Proceedings of the 26th Annual IFIP WG 11.3 conference on Data and Applications Security and Privacy
| Hi-index | 0.00 |
Malware is one of the major security threats in computer and network environment. However, Signature-based approach that commonly used does not provide enough opportunity to learn and understand malware threats that can be used in implementing security prevention mechanisms. In order to learn and understand the malwares, behavior-based technique that applied dynamic approach is the possible solution for identification, classification and clustering the malwares. In the paper, we present a new approach for conducting behavior-based analysis of malicious programs. One experiment was conducted on the campus network to generate an analysis of current malware behaviors. The result shows that the most potential malware threats in campus network are worm and Trojan.