Sumav: A Scheme for Selecting a Keyword from Detection Names using Conditional Probability
Sangwon Kim, Bora Kim, Su‐Young Kim, Wookhyun Jung, Buyngmoo Lee, Eui Tak Kim · 2020
With the rapid increase of malware, the detection and recognition of malware are getting hard. In this paper, we propose a scheme for selecting the representative malware keyword from malicious files selected from multiple antivirus engines. Using a token policy and a conditional probability among the nodes, we solved the problem with manual effort in previous studies. Through the experiment using real dataset from VT, we proved that the accuracy of representativeness has improved.