An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Analysis of Computer Intrusions Using Sequences of Function Calls
IEEE Transactions on Dependable and Secure Computing
Signature Generation and Detection of Malware Families
ACISP '08 Proceedings of the 13th Australasian conference on Information Security and Privacy
Automated classification and analysis of internet malware
RAID'07 Proceedings of the 10th international conference on Recent advances in intrusion detection
Malware classification method via binary content comparison
Proceedings of the 2012 ACM Research in Applied Computation Symposium
| Hi-index | 0.00 |
Developing variants of malware is a common and effective method to avoid the signature detection of antivirus programs. Malware analysis and signature abstraction are essential technologies to update the detection signature DB for malware detection. Since most malware binary analysis processes are performed manually, malware binary analysis is a time-consuming job. Therefore, efficient malware classification can be used to speed up malware binary analysis. As malware variants of the same malware family may share a portion of their binary code, the sequences of instructions may be similar, or even identical. In this paper, we propose a malware classification method that uses instruction frequencies. Our test results show that there are clear distinctions among malware and normal programs.