Malware categorization using dynamic mnemonic frequency analysis with redundancy filtering

Abstract

The battle between malware developers and security analysts continues, and the number of malware and malware variants keeps increasing every year. Automated malware generation tools and various detection evasion techniques are also developed every year. To catch up with the advance of malware development technologies, malware analysis techniques need to be advanced to help security analysts. In this paper, we propose a malware analysis method to categorize malware using dynamic mnemonic frequencies. We also proposed a redundancy filtering technique to alleviate drawbacks of dynamic analysis. Experimental results show that our proposed method can categorize malware and can reduce storage overheads of dynamic analysis.